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Mansi Bhonsle. "Predictive Analytics for Mental Health: Machine Learning Approaches in the Tech Industry." Panamerican Mathematical Journal 35, no. 1s (2024): 276–86. http://dx.doi.org/10.52783/pmj.v35.i1s.2314.
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Mental health concerns in the technology sector have become increasingly prominent, drawing attention due to the industry's high-pressure environment and intense workloads. This study investigates the application of machine learning techniques to predict mental health conditions among technology industry professionals. The research utilizes a comprehensive dataset, analyzing factors such as work-life balance, job satisfaction, workplace environment, and individual well-being. The study implements and evaluates several Machine Learning approaches, including Logistic Regression, Random Forest, Decision Tree Classifier, KNN, Gradient Boosting Classifier, XGB Classifier, to determine the most effective method for predicting mental health outcomes. The results highlight the potential of predictive analytics in early identification, enabling organizations to implement proactive strategies to enhance mental wellness and improve employee productivity. Additionally, this investigation emphasizes the necessity of incorporating mental health support mechanisms in technology-driven workplaces to foster a more positive work environment. The paper concludes with a discussion of the study's limitations and potential avenues for improving mental health prediction models in the future.
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Voleti, Rohit, Stephanie M. Woolridge, Julie M. Liss, et al. "Language Analytics for Assessment of Mental Health Status and Functional Competency." Schizophrenia Bulletin 49, Supplement_2 (2023): S183—S195. http://dx.doi.org/10.1093/schbul/sbac176.
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Abstract Background and Hypothesis Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling. Study Design Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer. Study Results Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables. Conclusions Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency.
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Khaliq, Syed Abdul. "Cutting-Edge Mental Health Evaluation and Tracking System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43046.
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Mental health disorders are common, and novel solutions for evaluation and tracking of these disorders are being sought. In this work, we describe a deep learning-based framework that incorporates AI-assisted self-assessment tools and real-time mental health monitoring. Using advanced feature extraction techniques and a wide array of machine learning models, we make predictions about mental health trends to enable timely interventions. Data-driven mental health overview will provide an efficient user experience while balancing the professional perception of the individual. This system encompasses predictive analytics, personalized interventions, and real-time tracking mechanisms that work together to enhance mental health support and implement preventive measures against potential crises. Keywords: Mental Health AI; Deep Learning; Self-Assessment; Predictive Analytics; Feature Extraction; Crisis Prevention
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TOM, SOORYA MERIN. "Harnessing Data Analytics for Enhanced Understanding and Management of Depression Disorders in Mental Health Care." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem36862.
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Depression is a widespread and incapacitating mental health condition that affects millions of people around the world.The integration of data analytics into mental health care presents a transformative opportunity to enhance the understanding, diagnosis, and management of depression. This paper explores the application of data analytics in identifying patterns and trends from diverse data sources such as electronic health records (EHRs) and social media. Through advanced techniques including machine learning, natural language processing (NLP), and predictive modeling, data analytics facilitates early detection of depressive symptoms, the development of personalized treatment plans, and continuous patient monitoring. Additionally, the aggregation and analysis of large- scale data provide valuable insights for public health strategies aimed at reducing the prevalence and impact of depression. This paper highlights the critical role of interdisciplinary collaboration in leveraging data analytics to improve mental health outcomes and underscores the importance of robust safeguards to ensure patient confidentiality and trust. Keywords Data Analytics, Depression, Mental Health, Predictive Modeling, Machine Learning, Natural Language Processing, Electronic Health Records
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Hahn, T., A. A. Nierenberg, and S. Whitfield-Gabrieli. "Predictive analytics in mental health: applications, guidelines, challenges and perspectives." Molecular Psychiatry 22, no. 1 (2016): 37–43. http://dx.doi.org/10.1038/mp.2016.201.
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Gupta, Megha. "HR Analytics: Trend from Data to Predictive Analysis for HR Professionals." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 2674–82. http://dx.doi.org/10.37200/ijpr/v24i5/pr201969.
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Oluwayemisi, A. Owoade, C. Moneke Kenechukwu, and C. Anioke Sandra. "Leveraging Business Intelligence to Optimize Resource Allocation in Mental Health and Substance Abuse Centers." Journal of Scientific and Engineering Research 9, no. 12 (2022): 210–35. https://doi.org/10.5281/zenodo.15044680.
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Introduction: Mental health and substance abuse disorders are two of the largest global burdens for the health care system. Management of resources is central to tackling these problems and enhance access to health. There is a great opportunity to use Business Intelligence (BI) tools and tools for predictive analytics to solve this task and form an effective decision-making system based on data. Besides, this research seeks to establish how BI can be utilised in mental health facilities in order to optimise the use of resources, patients’ access of care and general patient outcomes. This paper reviews literature on the use of BI, predictive analytics and mental health resources management researches. Since the study focuses on both qualitative and quantitative analysis, extensive search was made in both Google Scholar, Emerald Insight, Wiley Online Library for articles, book chapters, and industry reports. Business intelligence, resource management, mental and substance use disorders, prediction and analytics, are the keywords used in the work with their variations. The article underscores the possibility of applying BI-driven, predictive analytics for purposes of demand forecasting for mental and substance abuse services and thereby, directing corresponding resource provisioning. BI dashboards and data visualization approaches help show current levels of service delivery, congestion, or accumulation of resources, allowing administrators to make appropriate choices. Further, BI when connected with electronic health information and other databases, can point to trends and interaction which would help deliver appropriate care with efficient efficiency of utilisation of all resources available, implementation of BI tools to address patient scheduling, medication management, and staffing needs, achieving a 25% reduction in resource waste and a 30% improvement in service delivery times. The delivery and utilization of Mental Health Resources, therefore, hinges on the BI leveraging which is a multitude of Technological, Organisational and Strategic factors. Implementation aspects include the Integration of data, analytics abilities, ease of use interfaces and a data-focused culture. Issues of data privacy, security and ethical issues pose some of the risks that need to be well managed to promote the use of BI in mental health. BI’s integration in the management of mental health and substance abuse centers can open doors to improvement in terms of management of resources, accessibility and the general quality of services offered. With help of advanced methods of analytics and choosing the right solutions, these facilities will be able to allocate efforts and focus on the needs of the communities, therefore improving the quality of the people’s life. These findings underscore the potential for BI-driven frameworks to mitigate risks such as staffing shortages and medication supply issues, ensuring continuity of care during crises like pandemics or natural disasters. This research contributes to the intersection of healthcare management and supply chain resilience by demonstrating how advanced analytics can create agile, scalable systems for managing resources in mental health and substance abuse services.
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Ademeji, Florence, Emmanuel Okoro, Gbenga Akingbulere, Tosin Clement, and Stanley Okoro. "Predictive Analytics for Healthcare Resource Allocation in Underserved Communities." Journal of Research in Engineering and Computer Sciences 2, no. 6 (2024): 21–37. https://doi.org/10.63002/jrecs.26.726.
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Undeserved communities are usually at the receiving ends of resources allocation, particularly healthcare resources, which calls for understanding the key factors that contributes to efficient allocation of resources to the areas. This study investigates the use of predictive analytics for healthcare resource allocation in underserved communities. With the aid of predictive analytics, government can allocate resources effectively, which is sufficient enough to cater for the health needs of the people. The study adopted machine learning techniques, with 26 features included in the model to predict healthcare resource allocation. Data utilized in the study was collected from survey with the participants being residents in underserved communities. The study found that the important features are age, mental health status, visit to the hospital, and healthcare facilities quality, among others. It was recommended that Enhance mental health services and prioritize high-quality healthcare facilities to improve patient outcomes. Increase the availability and accessibility of healthcare providers, especially in areas with frequent hospital visits.
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Suzuki, K. "New platform of data analytics for mental health." European Psychiatry 33, S1 (2016): S33. http://dx.doi.org/10.1016/j.eurpsy.2016.01.863.
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IntroductionMental disorder is a key public health challenge and a leading cause of disability-adjusted life years (DALYs) due to its high level of disability and mortality. Therefore, a slight improvement on mental care provision and management could generate solid benefits on relieving the social burden of mental diseases.ObjectivesThis paper presents a long-term vision of strategic collaboration between Fujitsu Laboratories, Fujitsu Spain, and Hospital Clinico San Carlos to generate value through predictive and preventive medicine improving healthcare outcomes for every clinical area, benefiting managers, clinicians, and patients.AimsThe aim is to enable a data analytic approach towards a value-based healthcare system via health informatics. The project generates knowledge from heterogeneous data sources to obtain patterns assisting clinical decision-making.MethodsThis project leverages a data analytic platform named HIKARI (“light” in Japanese) to deliver the “right” information, to the “right” people, at the “right” time. HIKARI consists of a data-driven and evidence-based Decision Support and Recommendation System (DSRS), facilitating identification of patterns in large-scale hospital and open data sets and linking data from different sources and types.ResultsUsing multiple, heterogeneous data sets, HIKARI detects correlations from retrospective data and would facilitate early intervention when signs and symptoms prompt immediate actions. HIKARI also analyses resource consumption patterns and suggests better resource allocation, using real-world data.ConclusionsWith the advance of ICT, especially data-intensive computing paradigm, approaches mixing individual risk assessment and environmental conditions become increasingly available. As a key tool, HIKARI DSRS can assist clinicians in the daily management of mental disorders.Disclosure of interestThe author has not supplied his declaration of competing interest.
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Dileep, Valiki. "Revolutionizing Patient Outcomes: The Role of Generative AI and Machine Learning in Predictive Analytics for Healthcare." Journal of Artificial Intelligence and Big Data Disciplines 1, no. 1 (2024): 01–15. https://doi.org/10.70179/js9jft76.
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For the healthcare industry, predictive analytics offer revolutionary benefits for improving patient outcomes, reducing hospital readmissions, and lowering treatment costs. The increasing adoption of electronic health records allows the modeling of laboratory results, medications, and socio-economic data, as well as mental health, among others. We emphasize the opportunities that generative models offer for predictive healthcare analytics and the necessity for healthcare analytics to contextualize data relationships. We analyze predictive models, understand our contextual data relationships, interpret our results, expose them, and understand why models are learning certain relationships. We make use of benchmark data and case studies to illustrate our points. Our discussion concludes by offering a framework and a departure point for future related research.
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Aminat Tinuwonuola Durojaiye and Kenneth Udemezue katas. "Creating a predictive model for early detection, intervention, and prevention of opioid addiction in the United States." International Journal of Applied Research in Social Sciences 6, no. 11 (2024): 2587–610. http://dx.doi.org/10.51594/ijarss.v6i11.1691.
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The opioid crisis in the United States has escalated into a public health emergency, requiring innovative strategies for early detection, intervention, and prevention of addiction. This paper proposes the development of a predictive model leveraging machine learning algorithms and big data analytics to identify individuals at high risk of opioid addiction. The model integrates various data sources, including electronic health records (EHRs), prescription drug monitoring programs (PDMPs), socioeconomic factors, and behavioral health data, to create a comprehensive risk assessment tool. By employing predictive analytics, healthcare providers and policymakers can proactively identify individuals with high addiction risk before they develop opioid use disorders, enabling timely intervention. The model prioritizes early detection by utilizing risk factors such as previous substance use, mental health conditions, and prescription patterns. Additionally, the model focuses on intervention strategies that are personalized to the individual's needs, incorporating social support, mental health treatment, and safe prescribing practices. Prevention efforts are enhanced by using predictive insights to inform public health campaigns and policy adjustments, targeting vulnerable populations with tailored messaging and resources. This predictive model aims to address the limitations of reactive approaches to opioid addiction, which often fail to prevent addiction at its root causes. By combining advanced analytics with clinical expertise, the model provides a more proactive solution that can improve patient outcomes and reduce the burden on healthcare systems. Furthermore, ethical considerations, such as data privacy, accuracy, and bias mitigation, are integral to the model's development to ensure responsible implementation. In conclusion, creating a predictive model for opioid addiction represents a critical step in the fight against the opioid epidemic, offering opportunities for early detection, targeted intervention, and effective prevention strategies. Keywords: Opioid Addiction, Predictive Model, Early Detection, Machine Learning, Intervention, Prevention, Public Health, Data Analytics, Electronic Health Records, Prescription Drug Monitoring.
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Abdullah, Mokhtar, Mohammad Omar, and Tun Izlizam Bahardin. "Predictive Analytics of Mental Health Problems Among College Students in the Covid-19 Pandemic." Archives of Business Research 10, no. 11 (2022): 301–18. http://dx.doi.org/10.14738/abr.1011.13510.
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Covid-19 pandemic has brought into focus the mental health of various segments of society. This paper presents an empirical study on mental health of college students due to the specific situations caused by the pandemic. A particular focus is on the effects of three stressors, namely, academic workload, separation from school, and fears of contagion among the college students. This initiative was a follow-up of the study by Yang et al. (2021) who proposed a research model that evaluates the impacts of these stressors on perceived stress which subsequently affects the mental health of the students. Using the data collected by Yang et al. (2021), an alternative predictive analytics approach, i.e., Partial Least Squares Structural Equation Modelling (PLS-SEM), was adopted to re-evaluate the research model. This has produced an improvement over the results of Confirmatory Factor Analysis (CFA) adopted by Yang et al. (2021), particularly in the test for the significance of correlation between academic workload and mental health. While the use of PLS-SEM that allowed for strategic refinements of the model produced a significant correlation between the two important constructs, the CFA failed to obtain a similar result. All the other significant correlations between the stressors and mental health and correlation between the mediating factor, the perceived stress, and mental health were also established using the PLS-SEM approach.
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Satya Kiranmai Tadepalli. "Predictive Analytics in Mental Health: A Machine Learning Approach to Assessing Depression Severity." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 357–66. https://doi.org/10.52783/jisem.v10i1s.164.
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Contemporary healthcare reforms are being significantly shaped by the ongoing advance-ments in technology. One area where these advancements are proving crucial is in the un-derstanding and treatment of depression, which is increasingly emerging as a substantial public health concern. To address this issue, there is a growing interest in leveraging novel research methods and therapeutic approaches to identify the contributing factors to de-pression. This study adopts an innovative approach, utilizing machine learning techniques to carry out an exhaustive examination of diverse data sources. The primary aim is to gain a profound comprehension of the complex interplay between various facets of individuals' quality of life and the presence of depression. To undertake this investigation, the research-ers have harnessed the National Health and Nutrition Examination Survey (NHANES) Da-taset provided by the Centers for Disease Control and Prevention, a rich source of valuable health-related information.In this study, the focus is on exploring the behavioral and social dimensions of numerous subjects and their intricate connections to depression. To achieve this, a diverse array of machine learning classifiers has been deployed, including the Decision Tree Classifier, Ran-dom Forest Classifier, Gaussian Naive Bayes, KNN Classifier, Logistic Regression, Support Vector Machine Classifier, and Multilayer Perceptrons. By applying these classifiers, the re-searchers aim to assess their performance across various metrics, providing valuable insights into which models are best suited to discern the connection between depression and as-pects of quality of life.
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Shortreed, Susan M., and Gregory E. Simon. "Using predictive analytics to improve pragmatic trial design." Clinical Trials 17, no. 4 (2020): 394–401. http://dx.doi.org/10.1177/1740774520910367.
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Clinical trials embedded in health systems can randomize large populations using automated data sources to determine trial eligibility and assess outcomes. The suicide prevention outreach trial used real-world data for trial design and randomized 18,868 individuals in four health systems using patient-reported thoughts of death or self-harm (Patient Health Questionnaire item 9). This took 3.5 years. We consider if using predictive analytics, that is, suicide risk estimates based on prediction models, could improve trial “efficiency.” We used data on mental health outpatient visits between 1 January 2009 and 30 September 2017 in seven health systems (HealthPartners; Henry Ford Health System; and Colorado, Hawaii, Northwest, Southern California, and Washington Kaiser Permanente regions). We used a suicide risk prediction model developed in these same systems. We compared five trial designs with different eligibility criteria: a response of a 2 or 3 on Patient Health Questionnaire item 9, a response of a 3, suicide risk score above 90th, 95th, or 99th percentile. We compared the sample that met each criterion, 90-day suicide attempt rate following first eligible visit, and necessary sample sizes to detect a 15%, 25%, and 35% relative reduction in the suicide attempt rate, assuming 90% power, for each eligibility criterion. Our sample included 24,355,599 outpatient visits. Despite wide-spread use of Patient Health Questionnaire, 21,026,985 (86.3%) visits did not have a recorded Patient Health Questionnaire. Of the 2,928,927 individuals in our sample, 109,861 had a recorded Patient Health Questionnaire item 9 response of a 2 or 3 over the study years with a 1.40% 90-day suicide attempt rate and 50,047 had a response of a 3 (suicide attempt rate 1.98%). More patients met criteria requiring a certain risk score or higher: 331,273 had a 90th percentile risk score or higher (suicide attempt rate: 1.36%); 182,316 a 95th percentile or higher (suicide attempt rate 2.16%), and 78,655 a 99th percentile or higher (suicide attempt rate: 3.95%). Eligibility criterion of a Patient Health Questionnaire item 9 response of a 2 or 3 would require randomizing 44,081 individuals (40.2% of eligible population in our sample); eligibility criterion of a 3 would require 31,024 individuals (62.0% of eligible population). Eligibility criterion of a suicide risk score of 90th percentile or higher would require 45,675 individuals (13.8% of eligible population), 95th percentile 28,699 individuals (15.7% of eligible population), and 99th percentile 15,509 (19.7% of eligible population). A suicide risk prediction calculator could improve trial “efficiency”; identifying more individuals at increased suicide risk than relying on patient-report. It is an open scientific question if individuals identified using predictive analytics would respond differently to interventions than those identified by more traditional means.
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Mindell, Michael, and Taylor Smith. "Revolutionizing Student Health: A Review of Deep Learning Applications in Early Diagnosis and Personalized Monitoring." EDUTREND: Journal of Emerging Issues and Trends in Education 2, no. 1 (2025): 48–58. https://doi.org/10.59110/edutrend.509.
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The integration of deep learning methodologies with predictive analytics has demonstrated significant promise in enhancing student health outcomes. This study offers a comprehensive analysis of contemporary trends in predictive analytics and the implementation of deep learning methodologies. The examined studies indicate that deep learning models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), display significant accuracy and efficiency in early disease detection, mental health forecasting, and individualized health monitoring. Significant findings encompass the enhanced prediction precision of hybrid models, the proficiency of deep learning in managing intricate and sequential data, and the opportunity for early intervention via tailored health insights. Nonetheless, obstacles include inadequate data quality, algorithmic biases, and model interpretability persist as significant concerns. The implementation of deep learning models necessitates ethical considerations and openness. Subsequent research ought to concentrate on tackling these problems and broadening the utilization of deep learning across varied student demographics and health circumstances. The results indicate that deep learning can markedly improve early diagnosis, treatment optimization, and overall health outcomes for students, presenting a promising strategy for enhancing student health.
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Singh, Aman, Priya Mourya, Vijay Singh, Anurag Yesansure, and Shobha Bamane. "Mental Health Support Chatbot." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02489.
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Mental health conditions, including stress, anxiety and depression, are on the rise in workplaces and in the workplace, often as a result of today's fast-paced, high-pressure work environments. Access to quality mental healthcare suffers from social stigma, geographic barriers, and the high costs associated with treatment, resulting in many people going without proper support. Utilizing artificial intelligence and natural language processing, this study provides a comprehensive framework for a can chatbot of little cost to patients, available around the clock and created to aid in individualize mental wellness problems. The chatbot uses advanced machine learning algorithms to analyse user emotions, especially leveraging the power of DistilBERT and actions using OpenCV to detect face expression. The bot uses emotion detection and NLP skills and customize response as per emotional state of the user to ensure empathetic and situation appropriate conversation. Thus, a chatbot system that is powered by past data along with facts and AI Integration can totally change the way mental health issues are looked upon by providing users with continuous, scalable, and humanized care. Keywords: Natural Language Processing (NLP), OpenCV, Emotion Detection, AI-Powered Chatbots, DistilBERT, Text-Based Interactions, Stigma-Free Therapy, Personalized Support, Predictive Mental Health Support, and Real-Time Analytics.
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Naga, Santhosh Reddy Vootukuri. "AI-Driven Unified Framework for Mental Health Data." International Journal in Engineering Sciences 2, no. 4 (2025): 1–8. https://doi.org/10.5281/zenodo.15179470.
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Integrating Artificial Intelligence (AI) into the mental health field is revolutionizing the profession, with greater accuracy of diagnosis and room for customized intervention therapies. The traditional practice of mental healthcare through personal opinions and reactive treatments with resulting late diagnoses is replaced by data-driven, scalable solutions provided by AI-based platforms. This paper is a continuation of Chavali's (2024)[1] research on a "Unified Data Integration and Record Identification Framework" and suggests a revised framework adapted for mental health data. Building on AI-enabled data standardization, predictive analytics, and ethical use of AI, this framework provides solutions to fundamental problems in mental healthcare, such as data fragmentation, privacy, and model interpretability.
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J.Archenaa and Anita E.A.Mary. "Health Recommender System using Big data analytics." Journal of Management Science and Business Intelligence 2, no. 2 (2017): 17–24. https://doi.org/10.5281/zenodo.835606.
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This paper gives an insight on how to use big data analytics for developing effective health recommendation engine by analyzing multi structured healthcare data. Evidence-based medicine is a powerful tool to help minimize treatment variation and unexpected costs. Large amount of healthcare data such as Physician notes, medical history, medical prescription, lab and scan reports generated is useless until there is a proper method to process this data interactively in real-time. In this world filled with the latest technology, healthcare professionals feel more comfortable to utilize the social network to treat their patients effectively. To achieve this we need an effective framework which is capable of handling large amount of structured, unstructured patient data and live streaming data about the patients from their social network activities. Apache Spark plays an effective role in making meaningful analysis on the large amount of healthcare data generated with the help of machine learning components and in-memory computations supported by spark. Healthcare recommendation engine can be developed to predict about the health condition by analyzing patient’s life style, physical health factors, mental health factors and their social network activities. Machine learning algorithms plays an essential role in providing patient centric treatments. Bayesian methods is becoming popular in medical research due its effectiveness in making better predictions.For example on training the model with the age of women and diabetes condition helps to predict the chances of getting diabetes for new women patients without detailed diagnosis.
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Varshney, Monika. "DECREASE HEALTH ISSUES BY BIG DATA ANALYSIS." Journal of Science Innovations and Nature of Earth 3, no. 3 (2023): 09–12. http://dx.doi.org/10.59436/https://jsiane.com/archives3/3/93.
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The goal of "Swastha Bharat," or a healthy India, is to help every Indian realise his or her dream of living in an incredible India. Article 21 of the Constitution of India guarantees every citizen the right to health care. Health, as defined by the World Health Organisation (WHO), is not only the absence of disease but rather the whole mental, physical, and social flourishing of a person. New computer technologies have had a profound impact on the health industry, driving the generation of more medical data and spawning new subdisciplines of study. In order to make Swastha Bharat (Healthy India) a reality, this article discusses the crucial computing and analytical capabilities of Big Data in handling massive amounts of transactional information in real time. A universally applicable system is proposed. Keywords: Big Data Analytics, Swastha Bharat, Big Data Challenges, e-Health Care, Health Predictive Analysis, Health care in India.
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Chioma Ann Udeh, Omamode Henry Orieno, Obinna Donald Daraojimba, Ndubuisi Leonard Ndubuisi, and Osato Itohan Oriekhoe. "BIG DATA ANALYTICS: A REVIEW OF ITS TRANSFORMATIVE ROLE IN MODERN BUSINESS INTELLIGENCE." Computer Science & IT Research Journal 5, no. 1 (2024): 219–36. http://dx.doi.org/10.51594/csitrj.v5i1.718.
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In the dynamic landscape of modern business intelligence, Big Data Analytics has emerged as a transformative force, reshaping the way organizations derive insights from vast and diverse datasets. This paper provides a concise overview of the key themes explored in the comprehensive review of Big Data Analytics and its impact on modern business intelligence. Big Data Analytics represents a paradigm shift in decision-making processes, offering organizations the capability to harness the full potential of their data assets. The review delves into the multifaceted role of Big Data Analytics, emphasizing its significance in strategic planning, risk management, operational optimization, and customer-centric initiatives. Strategic planning takes a quantum leap forward as organizations leverage predictive analytics to anticipate market trends. The integration of analytics-derived insights aligns decision-making with overarching organizational objectives, driving a more informed and forward-thinking approach to strategic initiatives. Risk management becomes more proactive with the integration of Big Data Analytics, particularly in fraud detection and prevention. The ability to process large volumes of data in real-time enhances vigilance, mitigating financial risks associated with fraudulent activities cenario modeling further empowers organizations to assess and address potential risks before they materialize. Operational optimization becomes a focal point as analytics uncovers inefficiencies in manufacturing processes, supply chains, and retail operations. Real-time decision-making in retail, enabled by data analytics, ensures agility and responsiveness to changing market dynamics and customer preferences. Customer-centric initiatives are elevated through personalized marketing campaigns and predictive analytics in customer support. The review explores how Big Data Analytics enables organizations to craft personalized experiences, enhancing customer satisfaction and loyalty. The study encapsulates the transformative journey of Big Data Analytics in modern business intelligence, emphasizing its role in navigating strategic complexities, mitigating risks, optimizing operations, and placing the customer at the center of decision-making processes. The comprehensive review provides insights for organizations seeking to harness the transformative potential of Big Data Analytics in the data-driven era.
 Keywords: Big Data, Business Intelligence, Data Analytics, Modern Business, Review.
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Kumar, V. Pavan. "MindTrack: Machine Learning for Mental Health Insights." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02871.
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Mental health disorders affect millions of individuals worldwide, emphasizing the urgent need for early detection and intervention. MindTrack: Machine Learning for Mental Health Insights is an innovative tool designed to predict and assess mental health conditions such as depression, anxiety, and stress using advanced machine learning techniques. The system leverages user inputs, including textbased responses, survey data, and optional behavioral metrics, to analyze patterns and identify potential mental health risks. Preprocessed data is evaluated through robust algorithms, including natural language processing (NLP) models for text analysis and statistical models for numeric inputs, ensuring high accuracy and sensitivity. MindTrack offers actionable insights, such as self-care tips, progress tracking, and professional recommendations, all while ensuring data privacy and ethical standards. By integrating artificial intelligence with mental health care, the project aims to empower individuals and healthcare providers to address mental well-being proactively. MindTrack aims to bridge the gap between technology and mental health care, fostering a more inclusive and proactive approach to mental well-being. Keywords: Mental Health Detection, Machine Learning, Psychological Assessment, Behavioral Insights, Supervised Learning, Feature Engineering, Model Optimization, Hyperparameter Tuning, Front-End Integration, GUI-Based Input, Predictive Analytics, Health Monitoring, Data-Driven Insights, Depression.
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Hudson, Christopher G. "Benchmarks for Needed Psychiatric Beds for the United States: A Test of a Predictive Analytics Model." International Journal of Environmental Research and Public Health 18, no. 22 (2021): 12205. http://dx.doi.org/10.3390/ijerph182212205.
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The ideal balanced mental health service system presupposes that planners can determine the need for various required services. The history of deinstitutionalization has shown that one of the most difficult such determinations involves the number of needed psychiatric beds for various localities. Historically, such assessments have been made on the basis of waiting and vacancy lists, expert estimates, or social indicator approaches that do not take into account local conditions. Specifically, this study aims to generate benchmarks or estimated rates of needed psychiatric beds for the 50 U.S. states by employing a predictive analytics methodology that uses nonlinear regression. Data used were secured primarily from the U.S. Census’ American Community Survey and from the Substance Abuse and Mental Health Administration. Key predictors used were indicators of community mental health (CMH) service coverage, mental health disability in the adult population, longevity from birth, and the percentage of the 15+ who were married in 2018. The model was then used to calculate predicted bed rates based on the ‘what-if’ assumption of an optimal level of CMH service availability. The final model revealed an overall rate of needed beds of 34.9 per 100,000 population, or between 28.1 and 41.7. In total, 32% of the states provide inpatient psychiatric care at a level less than the estimated need; 28% at a level in excess of the need; with the remainder at a level within 95% confidence limits of the estimated need. These projections are in the low range of prior estimates, ranging from 33.8 to 64.1 since the 1980s. The study demonstrates the possibility of using predictive analytics to generate individualized estimates for a variety of service modalities for a range of localities.
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Vivek, Yadav. "AI and Economics of Mental Health: Analyzing how AI can be used to improve the cost-effectiveness of mental health treatments and interventions." Journal of Scientific and Engineering Research 8, no. 7 (2021): 274–84. https://doi.org/10.5281/zenodo.13600238.
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The implementation of AI in Mental health presents a chance to revolutionize the field in terms of the cost structure of the interventions made. Analyzing the capacity of AI technologies to deliver accessible and accurate mental health care as well as drawing the relationship between data analytics and prognostications, individualized consumer care plans, and resource Utilization in healthcare settings is this paper’s objective. Based on the literature review, this research established the following broad research questions: This methodology entails the use of approaches in the current innovation and technologies with specific reference to artificial intelligence for mental health and analyzing their efficiency. Some of the findings that stand out include the realization of great cost reduction and optimality in disease treatment… Attention is paid to such aspects as the following concerns of the healthcare providers, policymakers, and patients: The directions of further development of AI-based mental health solutions Proper time and amount of investment in the given directions This paper suggests directions for future research and concrete actions that will endeavor towards the improvement of the south Asian country’s mental health system through integration of artificial intelligence.
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Siddhi Unawane, Shruti Shelke, Lavanya Gaikwad, Om Sonawane, and Bharti Ahuja. "Early Depression Detection Using AI: A Web-Based Psychiatrist-Patient Platform." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 854–59. https://doi.org/10.47392/irjaeh.2025.0121.
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Mental health disorders such as depression require continuous monitoring and timely intervention, yet existing solutions often lack real-time tracking and personalized care. The proposed system is a web-based portal designed to enhance mental health management by bridging the gap between psychiatrists and patients. It provides secure login and personalized dashboards, featuring therapeutic videos on themes like depression and emotional well-being. The system employs machine learning techniques, particularly the Random Forest algorithm, to track patient activity, including video engagement and interaction patterns, for early detection of potential mental health concerns. The Random Forest model is chosen for its high classification accuracy and robustness in predicting behavioral trends. Upon identifying concerning behavioral patterns, the system generates alerts for psychiatrists, facilitating timely intervention. Additionally, the platform fosters improved communication, allowing psychiatrists to monitor patient progress, tailor treatment plans, and provide data-driven recommendations. By integrating AI-driven behavioral tracking, video therapy, and predictive analytics, this system aims to offer a proactive and personalized approach to mental health care.
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Sharma, Aadit, Bolaji Iyanu Adekunle, Jeffrey Chidera Ogeawuchi, Abraham Ayodeji Abayomi, and Omoniyi Onifade. "AI-Driven Patient Risk Stratification Models in Public Health: Improving Preventive Care Outcomes through Predictive Analytics." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 3 (2023): 1123–30. https://doi.org/10.54660/.ijmrge.2023.4.3.1123-1130.
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This paper explores the transformative role of artificial intelligence (AI) in patient risk stratification within public health, emphasizing its potential to improve preventive care outcomes through predictive analytics. AI technologies, particularly machine learning models, enable healthcare systems to predict patient health risks, enhance diagnostic accuracy, and optimize resource allocation. By analyzing vast amounts of patient data, AI can identify high-risk individuals for chronic diseases, mental health conditions, and other health crises, allowing for timely and targeted interventions. Case studies are presented to illustrate AI’s effectiveness in early disease detection, mental health risk identification, and large-scale population health management. Furthermore, the integration of AI in healthcare is shown to contribute to cost-effectiveness by reducing hospital readmissions, streamlining workflows, and preventing the progression of preventable diseases. Ethical and regulatory considerations are discussed, addressing concerns such as data privacy, algorithmic bias, and transparency. Future directions for AI in public health, including the integration with emerging technologies and the development of explainable models, are also explored. Finally, policy implications are offered, advocating for frameworks to ensure the ethical use of AI while supporting research and workforce development to maximize AI’s impact in improving healthcare outcomes.
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Depp, Colin, John Torous, and Wesley Thompson. "Technology-Based Early Warning Systems for Bipolar Disorder: A Conceptual Framework." JMIR Mental Health 3, no. 3 (2016): e42. http://dx.doi.org/10.2196/mental.5798.
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Recognition and timely action around “warning signs” of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that early warning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for early warning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating early warning systems for bipolar disorder with a focus on pragmatic considerations.
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Mohite, Rajsinh V., Money Saxena, Prakash Kumar Sahoo, and Pooja Varma. "Implementing Advanced Analytics in Occupational Health for Real-Time Risk Assessment." Health Leadership and Quality of Life 2 (December 31, 2023): 242. https://doi.org/10.56294/hl2023242.
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Making ensuring that employees in all types of companies are safe and healthy mostly depends on occupational health. As companies strive for increased efficiency, it is rather crucial for them to identify and lower any potential hazards in the workplace. Though conventional methods of risk assessment have their uses, they are frequently sluggish, reactive, and unable of adjusting for changing work environments. This study article investigates how sophisticated data combined with real-time risk assessment could enhance health at work. Using machine learning, big data analytics, and predictive modelling, the project seeks to use real-time risk finding, evaluation, and control in workplace health systems. Using a range of data sources—environmental conditions, employee health records, statistics on equipment usage, and real-time monitoring of markers of physical and mental health—the paper offers a roadmap for applying advanced analytics technologies. Before they lead to accidents or diseases, the proposed system uses predictive analytics to identify health hazards and threats include weariness, exposure to hazardous substances, and excessive stress levels. Combining these technologies lets businesses respond before something occurs rather than waiting for it to happen. This keeps workers safer, reduces the likelihood of occupational mishaps, and enhances overall health management practices. The paper also addresses how screens and data visualisation could enable employees in the field of occupational health make better decisions. These instruments enable speedier action and help one to grasp complex data, hence accelerating the identification of high-risk patterns. Furthermore discussed in the paper are the probable advantages of applying artificial intelligence (AI) to identify new hazards, enhance workplace architecture, and streamline health initiatives for every worker. Emphasising how crucial it is for managers, data scientists, and workplace health specialists to collaborate across disciplines to create a good advanced analytics system, the study finishes Ultimately, this approach is supposed to improve the workplace by ensuring that employees are shielded from hazards that can be avoided, therefore promoting health.
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.Gnanapriya, Dr S. "Predicting Gut Health Using MI Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem41970.
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Digestion, immunity, and mental health are all impacted by gut health, which is crucial to general wellbeing. However, because the gut microbiota is dynamic and diverse, evaluating and forecasting gut health can be challenging. Through the analysis of microbiome data, food patterns, and lifestyle characteristics, this study investigates the potential of machine learning (ML) techniques to predict gut health. Overall health is greatly influenced by gut health, and imbalances in the gut microbiota have been connected to a number of illnesses. Predicting gut health based on food patterns, clinical indicators, and microbiome composition is made possible by machine learning (ML) techniques. This study investigates the classification and prediction of gut health status using the Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB) algorithms. To improve model accuracy, feature selection, data preparation methods, and hyperparameter tuning were used. Gradient Boosting surpassed RF and SVM in terms of predictive capability, according to performance evaluation utilizing measures including accuracy, precision, recall, and F1-score. According to the results, ML-driven methods can evaluate gut health in an efficient manner, offering insightful information for early illness detection and individualized treatment. Keywords: Gut health, microbiome, machine learning, gut microbiota, health analytics, feature engineering, personalized nutrition, data-driven healthcare.
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Radwan, Ahmad, Mohannad Amarneh, Hussam Alawneh, Huthaifa I. Ashqar, Anas AlSobeh, and Aws Abed Al Raheem Magableh. "Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis." International Journal of Web Services Research 21, no. 1 (2024): 1–22. http://dx.doi.org/10.4018/ijwsr.338222.
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The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach utilizing large language models (LLMs), with a focus on OpenAI's generative pre-trained transformer (GPT-3) embeddings and machine learning (ML) algorithms to classify social media posts as indicative or not of stress disorders. The aim is to create a preliminary screening tool leveraging online textual data. GPT-3 embeddings transformed posts into vector representations capturing semantic meaning and linguistic nuances. Various models, including support vector machines, random forests, XGBoost, KNN, and neural networks, were trained on a dataset of >10,000 labeled social media posts. The top model, a support vector machine, achieved 83% accuracy in classifying posts displaying signs of stress.
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Singh, Aman. "Mental Health Support Chatbot." International Scientific Journal of Engineering and Management 04, no. 06 (2025): 1–7. https://doi.org/10.55041/isjem03966.
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ABSTRACT In today's busy, high-pressure workplaces, mental health conditions like stress, anxiety, and depression are on the rise. Due to the high expense of treatment, geographic limitations, and social stigma, many people do not receive the proper support. This study offers a comprehensive framework for a cost-effective, AI-powered chatbot that promotes mental health and is accessible 24/7 to assist users with their unique mental wellness concerns. The chatbot makes use of advanced machine learning and natural language processing capabilities with the aid of OpenCV for facial emotion recognition and Langchain-integrated large language models (LLMs). The chatbot interprets user text input and facial expressions to customize responses based on emotional state, ensuring conversations that are human-like, sympathetic, and contextually aware. In contrast to conventional models that rely on fixed datasets, a powerful LLM empowers the chatbot to create contextually and emotionally aware responses in real-time without the need for static dataset training. The use of real-time analytics and ongoing monitoring enables user-level feedback and assessment of interactions, enhancing its responsiveness and adaptability. With the capacity to identify emotions such as joy, anger, or sadness, the chatbot can deliver appropriate support and guidance. An intuitive interface boosts accessibility and offers valuable insights for individuals seeking mental health assistance. This structured, privacy-focused approach offers a judgment-free environment where individuals can openly discuss their mental health challenges. The key advantages of the system include its timely availability, emotional awareness, and ability to scale. Future enhancements will focus on improving emotion detection, facilitating long-term contextual dialogues, and integrating voice communication to deliver a more engaging and interactive support experience. Consequently, a Langchain and real-time AI technology-based chatbot system has the potential to revolutionize the mental health care sector by delivering continuous, scalable, and highly personalized support. Keywords: Natural Language Processing (NLP), OpenCV, Emotion Detection, AI-Powered Chatbots, Langchain, Text-Based Interactions, Stigma-Free Therapy, Personalized Support, Predictive Mental Health Support.
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Nemade, Dr Sonali. "Machine Learning-Based Intervention Recommendation System for Student Stress Management." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03302.
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Abstract - The growing number of students facing mental health problems emphasizes how urgently proactive, individualized stress management strategies are needed. This study suggests a Machine Learning-Based Intervention Recommendation System (MLIRS) that predicts stress levels and suggests customized therapies by analyzing behavioral, academic, and physiological data from students. Academic records, wearable technology, and questionnaires are all integrated into the system to collect data. We use three supervised learning models to categorize stress levels: Random Forest, Support Vector Machine (SVM), and Gradient Boosting. A recommendation engine that combines collaborative and content-based filtering methods makes recommendations for relevant treatments. A real-world student dataset evaluation reveals a 92% prediction accuracy and excellent user satisfaction with the suggested interventions. Key Words: Student Stress, Machine Learning, Stress Detection, Intervention Recommendation, Mental Health, Hybrid Recommender System, Academic Performance, Predictive Analytics
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Joshua Ikenna Egerson, Idowu Oluwayoma Adeleke, Taiwo Akindahunsi, et al. "Data-driven approaches to tackling mental health." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 001–16. http://dx.doi.org/10.30574/wjarr.2024.23.3.2638.
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Background: Over the past few years there has been immense evolution in various areas particularly in the areas of digital technologies wherein the pace of change is very high. Industrial areas such as operations and supply chain management together with advanced technologies such as machine learning, big data analytics, artificial intelligence, as well as the Internet of Things, create completely different forms of operational models for various industries. In the area of healthcare too, these emerging computational sophistication is introduced to revolutionise the approaches to prevent, diagnose and treat diverse diseases and illnesses. Objective: The objective of this study is to provide an extensive review of the contemporary approaches utilizing data to cope with significant mental disorders. From over 60 relevant scholarly articles published between 2011 and 2023, it discusses how tools such as predictive modelling, social media analysis, data from smartphones, and chatbots help with issues such as early detection, telemonitoring, provision of psychological support, and individualised prevention. Method: An initial literature review to analyse over 60 research articles, which include empirical studies that were conducted between 2011 and 2023. The research assessed implemented novel digital approaches to mental health interventions including big data analytics for predicting condition status, machine learning for examining social media content, behaviour monitoring through smartphone sensors, and using conversational agents or chatbots. The following is an overview of general conclusions from experimental and descriptive secondary research studies published in professional outlets concerning possible advantages and disadvantages of data science applied to important concerns in mental health. Results: Research reveals that integrating subtle e-health tools in tandem with typical treatment approaches holds the potential to expand mental health services to more or less integrate them into clients’ day-to-day lives, and practically individualize effective treatments accordingly. Technological solutions for instance allow remote risk assessment, symptom monitoring and determination of treatment compliance. New lines of virtualized paradigm solve social challenges that interfere with the conventional provision and consumption of care. However, questions of privacy and the long-term effects as well as clinical adoption are yet to be solved in a analytically distinct manner. Discussion: Despite there is a great number of opportunities, certain critical issues need to be solved to unleash the full potential of the data-driven approach in mental health care. Namely, technology integration into the streams of a provider’s work assumes seamless compatibility and observable benefits. Appreciating population effects calls for steady long-term semantics assessments. For people of color to feel respected the designs and language used must be culturally appropriate. Resolving such barriers will strengthen trust and outcomes, thus merit focused efforts from technology designers, clinicians and policymakers.
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Joshua, Ikenna Egerson, Oluwayoma Adeleke Idowu, Akindahunsi Taiwo, et al. "Data-driven approaches to tackling mental health." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 001–16. https://doi.org/10.5281/zenodo.14908924.
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<strong>Background</strong>: Over the past few years there has been immense evolution in various areas particularly in the areas of digital technologies wherein the pace of change is very high. Industrial areas such as operations and supply chain management together with advanced technologies such as machine learning, big data analytics, artificial intelligence, as well as the Internet of Things, create completely different forms of operational models for various industries. In the area of healthcare too, these emerging computational sophistication is introduced to revolutionise the approaches to prevent, diagnose and treat diverse diseases and illnesses. <strong>Objective</strong>: The objective of this study is to provide an extensive review of the contemporary approaches utilizing data to cope with significant mental disorders. From over 60 relevant scholarly articles published between 2011 and 2023, it discusses how tools such as predictive modelling, social media analysis, data from smartphones, and chatbots help with issues such as early detection, telemonitoring, provision of psychological support, and individualised prevention. <strong>Method</strong>: An initial literature review to analyse over 60 research articles, which include empirical studies that were conducted between 2011 and 2023. The research assessed implemented novel digital approaches to mental health interventions including big data analytics for predicting condition status, machine learning for examining social media content, behaviour monitoring through smartphone sensors, and using conversational agents or chatbots. The following is an overview of general conclusions from experimental and descriptive secondary research studies published in professional outlets concerning possible advantages and disadvantages of data science applied to important concerns in mental health. <strong>Results</strong>: Research reveals that integrating subtle e-health tools in tandem with typical treatment approaches holds the potential to expand mental health services to more or less integrate them into clients’ day-to-day lives, and practically individualize effective treatments accordingly. Technological solutions for instance allow remote risk assessment, symptom monitoring and determination of treatment compliance. New lines of virtualized paradigm solve social challenges that interfere with the conventional provision and consumption of care. However, questions of privacy and the long-term effects as well as clinical adoption are yet to be solved in a analytically distinct manner. <strong>Discussion</strong>: Despite there is a great number of opportunities, certain critical issues need to be solved to unleash the full potential of the data-driven approach in mental health care. Namely, technology integration into the streams of a provider’s work assumes seamless compatibility and observable benefits. Appreciating population effects calls for steady long-term semantics assessments. For people of color to feel respected the designs and language used must be culturally appropriate. Resolving such barriers will strengthen trust and outcomes, thus merit focused efforts from technology designers, clinicians and policymakers.
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Awofala, Topeola Balkis, Adeola Bilikis Lateef, Imohimi Edwin, and Demilade Salam. "Data-driven approaches to mitigate academic stress and improve student mental health." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2201–6. https://doi.org/10.5281/zenodo.15221297.
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This paper critically examines the efficacy and transformative potential of data-driven methodologies to assess, monitor, and mitigate academic stress, thereby enhancing student mental health. We aim to uncover latent stress patterns and trigger points within academic environments by utilizing a robust framework of advanced analytics, machine learning, and predictive modeling. Applying these technologies allows for the strategic customization of interventions tailored to individual and group needs in real time. By synthesizing data across multiple educational settings—including K-12 schools and higher education institutions—this study provides comprehensive insights into how varied data sources and modeling techniques can be harmonized to effectively detect and address student stress. The outcomes highlighted in this paper demonstrate the significant impact of data-driven methodologies not only in improving student well-being but also in fostering an educational atmosphere that prioritizes mental health. Our findings underscore the critical role that technological integration in educational strategies plays in revolutionizing student support systems and setting a new standard for mental health care within academic institutions.
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Mackline Nuwasiima, Metogbe Patricia Ahonon, and Caleb Kadiri. "The Role of Artificial Intelligence (AI) and machine learning in social work practice." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 080–97. http://dx.doi.org/10.30574/wjarr.2024.24.1.2998.
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The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.
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Mackline, Nuwasiima, Patricia Ahonon Metogbe, and Kadir Caleb. "The Role of Artificial Intelligence (AI) and machine learning in social work practice." World Journal of Advanced Research and Reviews 24, no. 1 (2024): 080–97. https://doi.org/10.5281/zenodo.15004090.
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The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.
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Kumar, Akshi. "Machine learning for psychological disorder prediction in Indians during COVID-19 nationwide lockdown." Intelligent Decision Technologies 15, no. 1 (2021): 161–72. http://dx.doi.org/10.3233/idt-200061.
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As the world combats with the outrageous and perilous novel coronavirus, national lockdown has been enforced in most of the countries. It is necessary for public health but on the flip side it is detrimental for people’s mental health. While the psychological repercussions are predictable during the period of COVID-19 lockdown but this enforcement can lead to long-term behavioral changes post lockdown too. Moreover, the detection of psychological effects may take months or years. This mental health crisis situation requires timely, pro-active intervention to cope and persevere the Coro-anxiety (Corona-related). To address this gap, this research firstly studies the psychological burden among Indians using a COVID-19 Mental Health Questionnaire and then does a predictive analytics using machine learning to identify the likelihood of mental health outcomes using learned features of 395 Indian participants. The proposed Psychological Disorder Prediction (PDP) tool uses a multinomial Naïve Bayes classifier to train the model to detect the onset of specific psychological disorder and classify the participants into two pre-defined categories, namely, anxiety disorder and mood disorder. Experimental evaluation reports a classification accuracy of 92.15%. This automation plays a pivotal role in clinical support as it aims to suggest individuals who may need psychological help.
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Oluwafunmi Adijat Elufioye, Chinedu Ugochukwu Ike, Olubusola Odeyemi, Favour Oluwadamilare Usman, and Noluthando Zamanjomane Mhlongo. "AI-DRIVEN PREDICTIVE ANALYTICS IN AGRICULTURAL SUPPLY CHAINS: A REVIEW: ASSESSING THE BENEFITS AND CHALLENGES OF AI IN FORECASTING DEMAND AND OPTIMIZING SUPPLY IN AGRICULTURE." Computer Science & IT Research Journal 5, no. 2 (2024): 473–97. http://dx.doi.org/10.51594/csitrj.v5i2.817.
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This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector.
 Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.
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Ejuma Martha Adaga, Zainab Efe Egieya, Sarah Kuzankah Ewuga, Adekunle Abiola Abdul, and Temitayo Oluwaseun Abrahams. "TACKLING ECONOMIC INEQUALITIES THROUGH BUSINESS ANALYTICS: A LITERATURE REVIEW." Computer Science & IT Research Journal 5, no. 1 (2024): 60–80. http://dx.doi.org/10.51594/csitrj.v5i1.702.
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This literature review delves into the intersection of business analytics and its potential to address economic inequalities. In an era where data-driven decision-making is becoming ubiquitous, this study explores how organizations leverage business analytics to analyze, understand, and mitigate economic disparities. The review encompasses a diverse range of scholarly articles, research papers, and case studies, providing insights into the strategies, methodologies, and impact of utilizing business analytics to tackle economic inequalities. It transitions into an exploration of the role that business analytics plays in this context, emphasizing the power of data-driven insights to inform and influence decision-makers in various sectors. Examining how predictive modeling techniques within business analytics contribute to identifying patterns and trends that inform strategic decision-making aimed at reducing economic inequalities. Investigating how business analytics is applied to formulate and assess the effectiveness of policies designed to address economic disparities, with a focus on evidence-based decision-making. Analyzing case studies that showcase how businesses leverage analytics to adopt more inclusive practices in areas such as hiring, promotions, and supply chain management, contributing to the reduction of economic inequalities. Examining the ethical dimensions of employing business analytics in the pursuit of reducing economic inequalities, including issues related to privacy, consent, and bias mitigation. The literature review concludes with a synthesis of findings, identifying gaps in current research and proposing avenues for future exploration. By synthesizing diverse perspectives, methodologies, and empirical evidence, this literature review contributes to a comprehensive understanding of how business analytics can serve as a powerful tool in the collective effort to tackle economic inequalities on a global scale.
 Keywords: Business Analytics, Inequalities, Bias Mitigation, Technological Advancement, Review.
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Agnes Clare Odimarha, Sodrudeen Abolore Ayodeji, and Emmanuel Adeyemi Abaku. "MACHINE LEARNING'S INFLUENCE ON SUPPLY CHAIN AND LOGISTICS OPTIMIZATION IN THE OIL AND GAS SECTOR: A COMPREHENSIVE ANALYSIS." Computer Science & IT Research Journal 5, no. 3 (2024): 725–40. http://dx.doi.org/10.51594/csitrj.v5i3.976.
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Machine Learning (ML) is revolutionizing supply chain and logistics optimization in the oil and gas sector. This comprehensive analysis explores how ML algorithms are reshaping traditional practices, leading to more efficient operations and cost savings. ML enables predictive analytics, demand forecasting, route optimization, and inventory management, improving overall supply chain performance. Supply chain and logistics in the oil and gas sector are inherently complex, involving numerous interconnected processes and stakeholders. ML algorithms are adept at handling this complexity by analyzing vast amounts of data to identify patterns and optimize operations. By leveraging historical data, ML can predict future demand, enabling companies to adjust their inventory levels and production schedules accordingly. ML algorithms also play a crucial role in route optimization, helping companies minimize transportation costs and reduce carbon emissions. By analyzing factors such as traffic patterns, weather conditions, and road conditions, ML algorithms can determine the most efficient routes for transporting goods and equipment. Furthermore, ML enables predictive maintenance, which is essential in the oil and gas sector to prevent equipment failures and downtime. By analyzing sensor data from equipment, ML algorithms can predict when maintenance is required, allowing companies to schedule maintenance proactively and avoid costly disruptions. In conclusion, ML is transforming supply chain and logistics optimization in the oil and gas sector by enabling predictive analytics, demand forecasting, route optimization, and predictive maintenance. By leveraging the power of ML, companies in the oil and gas sector can improve operational efficiency, reduce costs, and enhance overall supply chain performance.
 Keywords: Machine’s Learning, Supply Chain, Logistics, Optimization, Oil and Gas.
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Jaishankar Inukonda. "Harnessing Data for Continuous Improvement of the Whole Health Index in Integrated Care Models." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 2 (2024): 560–70. https://doi.org/10.32628/ijserset242433.
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The Whole Health Index (WHI) is a multidimensional measure that assesses patient outcomes and care quality by integrating physical, mental, and social health indicators. In the context of integrated care models, the effective use of data is critical to continuously improving WHI metrics. This article explores how data-driven approaches enhance the WHI by leveraging advanced analytics, interoperability, and predictive modeling. It discusses key strategies for data collection, integration, and utilization to optimize care delivery and improve patient outcomes. Additionally, the article highlights challenges such as data privacy, standardization, and implementation and proposes actionable solutions for overcoming these barriers.
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Haghighat, Parian, Denisa Gándara, Lulu Kang, and Hadis Anahideh. "Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22076–86. http://dx.doi.org/10.1609/aaai.v38i20.30211.
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Predictive analytics has been widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomprehensible to the officials who use them, reducing their trust and utility. Furthermore, predictive models may introduce or exacerbate bias and inequity, as they have done in many sectors of society. Therefore, there is a need for transparent, interpretable, and fair predictive models that can be easily adopted and adapted by different stakeholders. In this paper, we propose a fair predictive model based on multivariate adaptive regression splines (MARS) that incorporates fairness measures in the learning process. MARS is a non-parametric regression model that performs feature selection, handles non-linear relationships, generates interpretable decision rules, and derives optimal splitting criteria on the variables. Specifically, we integrate fairness into the knot optimization algorithm and provide theoretical and empirical evidence of how it results in a fair knot placement. We apply our fairMARS model to real-world data and demonstrate its effectiveness in terms of accuracy and equity. Our paper contributes to the advancement of responsible and ethical predictive analytics for social good.
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Zamani, Sanaz, Minh Nguyen, and Roopak Sinha. "Integrating Environmental Data for Mental Health Monitoring: A Data-Driven IoT-Based Approach." Applied Sciences 15, no. 2 (2025): 912. https://doi.org/10.3390/app15020912.
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Mental health disorders constitute a significant global challenge, compounded by the limitations of traditional management approaches that rely heavily on subjective self-reports and infrequent professional evaluations. This study presents a groundbreaking IoT-based system that integrates big data analytics, fuzzy logic, and machine learning to revolutionise mental health monitoring. In contrast to existing solutions, the proposed system uniquely incorporates environmental factors, such as temperature and humidity in enclosed spaces—critical yet often overlooked contributors to emotional well-being. By leveraging IoT devices to collect and process large-scale ambient data, the system provides real-time classification and personalised visualisation tailored to individual sensitivity profiles. Preliminary results reveal high accuracy, scalability, and the potential to generate actionable insights, creating dynamic feedback loops for continuous improvement. This innovative approach bridges the gap between environmental conditions and mental healthcare, promoting a transformative shift from reactive to proactive care and laying the groundwork for predictive environmental health systems.
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Ayoola, Ayoola A., Oloruntobi Funmilola Adeoye, Abayomi Ayoola, and Akinremi Joy. "Telepsychiatry and Digital Mental Health Interventions: The Future of Psychiatric Nursing." International Journal of Pharma Growth Research Review 2, no. 1 (2025): 28–38. https://doi.org/10.54660/ijpgrr.2025.2.1.28-38.
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The rapid advancement of digital health technologies is transforming psychiatric nursing, with telepsychiatry and digital mental health interventions emerging as essential tools for improving mental healthcare accessibility, efficiency, and patient outcomes. Telepsychiatry, a subset of telemedicine, enables remote psychiatric consultations via video conferencing, while digital interventions such as artificial intelligence (AI)-powered chatbots, mobile mental health apps, and wearable monitoring devices provide real-time support and symptom tracking. These innovations allow psychiatric nurses to deliver high-quality care to underserved populations, reduce wait times, and enhance continuous patient engagement through digital platforms. One of the most significant benefits of telepsychiatry is its ability to bridge the mental health gap, particularly in rural and low-resource settings where psychiatric services are scarce. Additionally, digital mental health tools facilitate early intervention, allowing nurses to monitor patients remotely and intervene before crises escalate. The integration of AI and machine learning in mental health diagnostics further enhances personalized care by predicting symptom progression and recommending targeted therapeutic strategies. However, despite these advancements, challenges such as data security, ethical concerns, and the digital divide remain critical barriers to widespread adoption. Ensuring compliance with privacy regulations and addressing disparities in technology access are essential for the equitable implementation of digital psychiatric nursing. Looking ahead, emerging innovations such as virtual reality (VR)-assisted therapy, augmented reality (AR)-based nursing education, and AI-driven predictive analytics will further revolutionize psychiatric care. As telepsychiatry and digital interventions become integral to mental health services, psychiatric nurses must adapt by acquiring new competencies in digital healthcare technologies. Strengthening policies, funding research, and enhancing digital literacy among psychiatric nurses will be crucial for ensuring the effectiveness and sustainability of telepsychiatry in the future of mental health care.
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Kessler, Ronald C. "The potential of predictive analytics to provide clinical decision support in depression treatment planning." Current Opinion in Psychiatry 31, no. 1 (2018): 32–39. http://dx.doi.org/10.1097/yco.0000000000000377.
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Oluwole Temidayo Modupe, Aanuoluwapo Ayodeji Otitoola, Oluwatayo Jacob Oladapo, et al. "REVIEWING THE TRANSFORMATIONAL IMPACT OF EDGE COMPUTING ON REAL-TIME DATA PROCESSING AND ANALYTICS." Computer Science & IT Research Journal 5, no. 3 (2024): 693–702. http://dx.doi.org/10.51594/csitrj.v5i3.929.
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Edge computing has emerged as a pivotal paradigm shift in the realm of data processing and analytics, revolutionizing the way organizations handle real-time data. This review presents a comprehensive review of the transformational impact of edge computing on real-time data processing and analytics. Firstly, the review delves into the fundamental concepts of edge computing, elucidating its architectural framework and highlighting its distinct advantages over traditional cloud-centric approaches. By distributing computational resources closer to data sources, edge computing mitigates latency issues and enhances responsiveness, thereby enabling real-time data processing at the edge. Furthermore, this review explores how edge computing facilitates the seamless integration of analytics capabilities into edge devices, empowering organizations to derive actionable insights at the source of data generation. Leveraging advanced analytics algorithms, such as machine learning and artificial intelligence, edge computing enables autonomous decision-making and predictive analytics in real time, fostering innovation across diverse industry verticals. Moreover, the review examines the transformative implications of edge computing on various sectors, including healthcare, manufacturing, transportation, and smart cities. By enabling localized data processing and analytics, edge computing enhances operational efficiency, ensures data privacy and security, and unlocks new opportunities for business optimization and value creation. This review underscores the profound impact of edge computing on real-time data processing and analytics, revolutionizing the way organizations harness data to drive informed decision-making and gain competitive advantage in today's dynamic business landscape. As edge computing continues to evolve, its transformative potential is poised to redefine the future of data-driven innovation and digital transformation.
 Keywords: Edge, Computing, Analytics, Data, Impact, Review.
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Vijay, Banothu, Lakshya Swarup, Ayush Gandhi, Sonia Mehta, Naresh Kaushik, and Satish Choudhury. "Evaluating the Impact of Machine Learning in Predictive Analytics for Personalized Healthcare Informatics." Seminars in Medical Writing and Education 3 (December 31, 2024): 502. https://doi.org/10.56294/mw2024502.
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By adding machine learning (ML) into predictive analytics, the area of personalised healthcare computing has evolved and new approaches to enhance patient outcomes via tailored treatment plans have been generated. This paper examines how healthcare treatments could be tailored and predicted using machine learning methods. It underlines how crucial sophisticated analytics are for enhancing patient care and guiding clinical choices. Treatment is more accurate, more efficient, and better generally when one can predict how a condition will worsen, choose the best course of action for taking drugs, and observe any issues. Like controlled and unstructured learning algorithms, machine learning models have proved to be able to efficiently examine large and complex clinical datasets including electronic health records (EHR) and genetic data. These models identify hidden trends, relationships, and patterns that enable us to forecast individual health paths, identify those at risk, and simplify preventive action. ML also makes it feasible to merge many kinds of data, therefore providing clinicians with a more complete picture of every patient's health and, ultimately, facilitates the provision of more individualised, better treatment. Many facets of healthcare, including management of chronic illnesses, cancer detection, mental health analysis, and new medication discovery, employ predictive models. By helping clinicians make decisions based on data, ML models assist to reduce errors and enhance the flow of treatment. Still, there are issues including concerns about data security, model understanding, and the necessity of consistent frameworks to ensure models are robust and dependable in real-life clinical environments. This work also addresses the moral issues raised by using machine learning algorithms in tailored healthcare. It addresses issues like prejudice, justice, and patient agreement. It emphasises the need of cooperation among legislators, data scientists, and healthcare professionals to maintain developing models so that the whole potential of machine learning in healthcare may be fulfilled.
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Awofala Topeola Balkis, Lateef Adeola Bilikis, Edwin Imohimi, and Salam Demilade. "Data-driven approaches to mitigate academic stress and improve student mental health." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2201–6. https://doi.org/10.30574/wjarr.2024.24.3.3930.
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This paper critically examines the efficacy and transformative potential of data-driven methodologies to assess, monitor, and mitigate academic stress, thereby enhancing student mental health. We aim to uncover latent stress patterns and trigger points within academic environments by utilizing a robust framework of advanced analytics, machine learning, and predictive modeling. Applying these technologies allows for the strategic customization of interventions tailored to individual and group needs in real time. By synthesizing data across multiple educational settings—including K-12 schools and higher education institutions—this study provides comprehensive insights into how varied data sources and modeling techniques can be harmonized to effectively detect and address student stress. The outcomes highlighted in this paper demonstrate the significant impact of data-driven methodologies not only in improving student well-being but also in fostering an educational atmosphere that prioritizes mental health. Our findings underscore the critical role that technological integration in educational strategies plays in revolutionizing student support systems and setting a new standard for mental health care within academic institutions.
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Aisha, Katsina Isa. "Exploring digital therapeutics for mental health: AI-driven innovations in personalized treatment approaches." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2733–49. https://doi.org/10.5281/zenodo.15241622.
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Digital therapeutics have emerged as a transformative approach in addressing mental health challenges, offering evidence-based, technology-driven interventions. As mental health disorders become increasingly prevalent globally, traditional methods of treatment often fail to meet the growing demand due to limited accessibility, stigmatization, and resource constraints. Digital therapeutics leverage advanced technologies, including artificial intelligence (AI), to bridge these gaps, providing scalable and personalized mental health solutions. AI has revolutionized this domain by enabling adaptive, data-driven interventions that cater to individual needs, ranging from mood disorders to complex conditions like post-traumatic stress disorder (PTSD) and depression. At a broader level, digital therapeutics represent a paradigm shift in healthcare, transitioning from generalized care models to highly personalized and proactive frameworks. AI-driven innovations, such as natural language processing (NLP), predictive analytics, and machine learning algorithms, have enhanced the efficacy of digital mental health tools by facilitating real-time monitoring, symptom analysis, and tailored therapeutic recommendations. These innovations integrate seamlessly with wearables, mobile applications, and virtual reality, providing patients with accessible and engaging platforms for mental health management. However, while AI-based digital therapeutics show immense promise, challenges remain. Ethical concerns about data privacy, bias in AI algorithms, and equitable access need to be addressed to maximize their potential. Additionally, integrating these tools into existing healthcare systems requires alignment with regulatory frameworks and clinician support. By narrowing the focus to personalized treatment approaches, this paper explores how AI-driven digital therapeutics can advance mental health care, providing actionable insights into creating more inclusive, effective, and accessible interventions.
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Sasi, Kiran Parasa. "Impact of AI in Employee Wellness and Well-being Programs." European Journal of Advances in Engineering and Technology 10, no. 5 (2023): 98–100. https://doi.org/10.5281/zenodo.13326295.
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Employee wellness and well-being programs are crucial for maintaining a healthy, productive workforce. The integration of Artificial Intelligence (AI) in these programs has revolutionized how organizations support their employees' physical, mental, and emotional health. This paper explores the impact of AI on employee wellness and well-being programs, focusing on personalized health recommendations, predictive analytics for early intervention, and real-time monitoring of wellness metrics. Through a comprehensive review of current literature and practical case studies, we analyze how AI-driven wellness programs enhance employee engagement, reduce healthcare costs, and improve overall organizational performance. Our findings also highlight the challenges associated with data privacy, ethical considerations, and the need for continuous improvement in AI algorithms. The study concludes with recommendations for leveraging AI to maximize the benefits of employee wellness programs while addressing associated challenges.