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Journal articles on the topic 'Predictive Mental Health Analytics'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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, D

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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 c

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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 encompas

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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 facilitat

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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

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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 pred

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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, benef

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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, i

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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 emplo

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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 stud

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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. Th

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Thomas, Raphael Shobi Andhikad. "AI-Centric Data Analytics in Public Behavioral Health Care." International Journal of Computing and Engineering 7, no. 14 (2025): 38–50. https://doi.org/10.47941/ijce.3005.

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Artificial intelligence-driven data intelligence represents a paradigm shift in public behavioral health care, offering unprecedented capabilities to extract meaningful insights from complex datasets. This integration transforms the delivery of mental health services by enabling personalized interventions, early risk detection, and optimized resource allocation across populations. The convergence of electronic health records, social determinants data, behavioral metrics, and wearable device inputs creates a comprehensive foundation for enhanced clinical decision-making. Through advanced patter

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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 Ja

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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

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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 individualiz

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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 A

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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

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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 Ind

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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, empha

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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

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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. s

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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 i

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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

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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.

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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 amo

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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 m

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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, clinica

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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 lingu

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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 recogni

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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 Machi

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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, diag

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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

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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 scho

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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 me

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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 me

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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

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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

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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 b

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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 analyzin

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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 highlight

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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 tra

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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-

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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 t

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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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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,

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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 iss

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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 adaptiv

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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 an

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