Relevant bibliographies by topics / Predictive Mental Health Analytics

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Predictive Mental Health Analytics'

Author: Grafiati
Published: 7 June 2025
Last updated: 12 July 2025

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Journal articles on the topic "Predictive Mental Health Analytics"

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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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Dissertations / Theses on the topic "Predictive Mental Health Analytics"

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Lin, Yu-Kai. "Health Analytics and Predictive Modeling: Four Essays on Health Informatics." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/555987.

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There is a marked trend of using information technologies to improve healthcare. Among all the health IT, electronic health record (EHR) systems hold great promises as they modernize the paradigm and practice of care provision. However, empirical studies in the literature found mixed evidence on whether EHRs improve quality of care. I posit two explanations for the mixed evidence. First, most prior studies failed to account for system use and only focused on EHR purchase or adoption. Second, most existing EHR systems provide inadequate clinical decision support and hence, fail to reveal the full potential of digital health. In this dissertation I address two broad research questions: a) Does meaningful use of EHRs improve quality of care? and b) How do we advance clinical decision making through innovative computational techniques of healthcare analytics? To these ends, the dissertation comprises four essays. The first essay examines whether meaningful use of EHRs improve quality of care through a natural experiment. I found that meaningful use significantly improve quality of care, and this effect is greater in historically disadvantaged hospitals such as small, non-teaching, or rural hospitals. These empirical findings present salient practical and policy implications about the role of health IT. On the other hand, in the other three essays I work with real-world EHR data sets and propose healthcare analytics frameworks and methods to better utilize clinical text (Essay II), integrate clinical guidelines and EHR data for risk prediction (Essay III), and develop a principled approach for multifaceted risk profiling (Essay IV). Models, frameworks, and design principles proposed in these essays advance not only health IT research, but also more broadly contribute to business analytics, design science, and predictive modeling research.
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Smith, Curtis. "The Role of Feedforward-Enabled Predictive Analytics in Changing Mental Models." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/492421.

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Business Administration/Management Information Systems<br>D.B.A.<br>One of the key determinants of an organization’s success is its ability to adapt to marketplace change. Given this reality, how do organizations survive or even thrive in today’s dynamic markets? The answer to this question is highly related to the adaptability of one of the organization’s key resource: its employees. Indeed, the central component of an organization’s success will depend on its ability to drive changes in the mental models of individual employees. Moreover, a critical facilitator of that will be the development of decision support tools that support change of those mental models. In response to this need there has been a tremendous growth in business analytic decision support tools, estimated to reach almost $200 billion in sales by 2019. The premise of this research is that these decision support tools are ill-suited to support true mental model change because they have focused on a feedback-enabled view and generally lack a predictive (feedforward-enabled) view of the likely outcomes of the decision. The purpose of this research is to study how changes in mental models can be facilitated through this feedforward mechanisms within the DSS tool. This research used a mixed method approach, leveraging the strengths of quantitative and qualitative research methodologies, to study this research question. The research showed that the feedforward-enabled DSS tool did create more mental model change and alignment (versus an ideal solution) compared to the control. The feedforward enabled tool also produced better alignment than the feedback-enabled decision support tool. In fact, the feedback-enabled decision support was shown to result in a poorer alignment with the ideal solution. This paper concludes by suggesting five areas for future research.<br>Temple University--Theses
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Cheng, Chih-Wen. "Development of integrated informatics analytics for improved evidence-based, personalized, and predictive health." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54872.

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Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.
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Lin, Yu-Kai, Hsinchun Chen, Randall A. Brown, Shu-Hsing Li, and Hung-Jen Yang. "HEALTHCARE PREDICTIVE ANALYTICS FOR RISK PROFILING IN CHRONIC CARE: A BAYESIAN MULTITASK LEARNING APPROACH." SOC INFORM MANAGE-MIS RES CENT, 2017. http://hdl.handle.net/10150/625248.

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Clinical intelligence about a patient's risk of future adverse health events can support clinical decision making in personalized and preventive care. Healthcare predictive analytics using electronic health records offers a promising direction to address the challenging tasks of risk profiling. Patients with chronic diseases often face risks of not just one, but an array of adverse health events. However, existing risk models typically focus on one specific event and do not predict multiple outcomes. To attain enhanced risk profiling, we adopt the design science paradigm and propose a principled approach called Bayesian multitask learning (BMTL). Considering the model development for an event as a single task, our BMTL approach is to coordinate a set of baseline models-one for each event-and communicate training information across the models. The BMTL approach allows healthcare providers to achieve multifaceted risk profiling and model an arbitrary number of events simultaneously. Our experimental evaluations demonstrate that the BMTL approach attains an improved predictive performance when compared with the alternatives that model multiple events separately. We also find that, in most cases, the BMTL approach significantly outperforms existing multitask learning techniques. More importantly, our analysis shows that the BMTL approach can create significant potential impacts on clinical practice in reducing the failures and delays in preventive interventions. We discuss several implications of this study for health IT, big data and predictive analytics, and design science research.
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McConnell, Meghan. "Advancements in the Evaluation and Implementation of Heart Rate Variability Analytics." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/404855.

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Clinical applications for heart rate variability (HRV) have become increasingly popular, gaining momentum and value as societies increased understanding of physiology reveals their true potential to reflect health. An additional reason for the rising popularity of HRV analysis, along with many other algorithmic based medical processes, is the relatively recent exponential increase of computing power and capabilities. Despite this many medical standards lag behind this booming increase in scientific knowledge, as the risks and precautions involved with healthcare necessarily take priority. Resultantly, the standards which pertain to the acceptable tolerance for accurate R-peak detection have remain unchanged for decades. For similar reasons, medical software is also prone to lag behind state-of-the-art developments. Yet, society is currently on the precipice of an age of high computational abilities, mass data storage, and capabilities to apply deep learning algorithms to reveal patterns that were previously inconceivable. So, when considering the needs of the future in relation to the place of HRV in healthcare, there is a distinct need for its accurate and precise collection, storage, and processing. In the work presented in this dissertation, the overarching aim was to increase the reliability of electrocardiogram (ECG) based HRV for use in predictive health analytics. To ensure both clarity and attainability, this project-level aim was broken down and addressed in a series of several works. The first a im w ork w as t o address the problems associated with the precision specified f or a ccurate p eak d etection, and thereby increase the reliability of predictive health analytics generated using HRV metrics. The study conducted around this initial aim investigates the specifics of match window requirements, clarifies the difference between fiducial marker and QRS complex detection, and makes recommendations on the precision required for accurate HRV metric extraction. In the second work, the aim was to ensure that there is a reliable foundation for the conduction of HRV-related research. Here, a thorough investigation of relevant literature revealed the lack of a suitable software, particularly for research requiring the analysis of large databases. Consequently, an improved HRV analysis platform was developed. Through use of both user-feedback and quantitative comparison to highly regarded software, the proposed platform is shown to offer a similar standard in estimated HRV metrics but requires significantly l ess manual e ffort (batch-processing approach) than the traditional single patient focused approach. The third work also addressed this aim, providing the base peak detection algorithm implemented within the HRV analysis platform. Experimentation undertaken here ensured that the developed algorithm performed precise fiducial marker detection, thereby increasing the reliability of the generated HRV metrics (measured against the framework presented in the first work). In the fourth work, the aim was to address the lack of published literature on the relationship between ECG sampling frequency (fs) and extracted HRV, in order to further ensure the reliability of predictive health analytics generated using HRV metrics. Here, a quantitative experimental approach was taken to evaluate the impact of ECG fs on subsequent estimations of HRV. This experimentation resulted in a recommendation for the minimum required ECG fs for reliable HRV extraction. The aim of the final work was to further improve the foundation for future predicative health analytics, by developing a robust pre-processing algorithm capable of autonomous detection of regions of valid ECG signal. This type of algorithm should be considered of critical importance to furthering machine learning (ML) based applications in the medical field. ML algorithms are heavily reliant on access to vast amounts of data, and without an automated pre-processing stage would require an unrealistic amount of hand-processing for implementation.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Eng & Built Env<br>Science, Environment, Engineering and Technology<br>Full Text
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Maas, Jenna. "Is the severity of mental health needs and the degree of mental health services received predictive of student retention at UW-Stout." Online version, 2008. http://www.uwstout.edu/lib/thesis/2008/2008maasj.pdf.

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Leis, Machín Angela 1974. "Studying depression through big data analytics on Twitter." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671365.

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Mental disorders have become a major concern in public health, since they are one of the main causes of the overall disease burden worldwide. Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Language is one of the main tools on which mental health professionals base their understanding of human beings and their feelings, as it provides essential information for diagnosing and monitoring patients suffering from mental disorders. In parallel, social media platforms such as Twitter, allow us to observe the activity, thoughts and feelings of people’s daily lives, including those of patients suffering from mental disorders such as depression. Based on the characteristics and linguistic features of the tweets, it is possible to identify signs of depression among Twitter users. Moreover, the effect of antidepressant treatments can be linked to changes in the features of the tweets posted by depressive users. The analysis of this huge volume and diversity of data, the so-called “Big Data”, can provide relevant information about the course of mental disorders and the treatments these patients are receiving, which allows us to detect, monitor and predict depressive disorders. This thesis presents different studies carried out on Twitter data in the Spanish language, with the aim of detecting behavioral and linguistic patterns associated to depression, which can constitute the basis of new and complementary tools for the diagnose and follow-up of patients suffering from this disease
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Muir, Amanda. "Prospective study of the mental ill-health of adults with intellectual disabilities : outcomes and predictive determinants." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4683/.

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Background: The prevalence of mental ill-health and problem behaviour within the intellectually disabled population is reported to range from 30 to 50%. However, the longer term outcomes of mental ill-health and problem behaviour, such as persistence, new onset, remission and resilience, are unknown. Accordingly, the factors predictive of such outcomes are also unknown. Aims: To determine the long term outcomes of mental ill-health and problem behaviour, and the factors predictive of and associated with such outcomes, over a 10 year time-period in a cohort of adults with mild to profound intellectual disabilities. Method: A population-based cohort of adults with intellectual disabilities (n=100) was investigated at three time points over a 10 year period. Data were collected using a range of measures. Descriptive statistics were derived and regression analyses performed to determine factors predictive of outcomes. Results: The rate of psychopathology was found to have increased in the cohort over the 10 year period. Factors predictive of this increase were experiencing an angry interaction and trusting to share a secret with only one person, or anyone. The majority of the cohort experienced episodic mental ill-health, with relapse being predicted by being female and experiencing life events. New onset of mental ill-health was predicted by experiencing life events, and resilience was predicted by not experiencing life events and having urinary continence. Problem behaviours were persistent in 50%, with 50% remitting. New onset of problem behaviours was predicted by not experiencing life events, and resilience was predicted by having mild intellectual disabilities, not experiencing an angry interaction and having more than one close friend. Small but significant negative correlations were found between psychopathology and participation in social, leisure, and peer activities. Findings should be interpreted with caution due to the small sample size. Conclusions: The present study is the only existing longitudinal investigation following an adult cohort with mild to profound intellectual disabilities, at several time points over a 10 year period. Therefore, future research is needed to confirm findings. Given the increase in psychopathology, more effective monitoring, treatment and intervention is needed.
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Sarri, Margarita. "Factors predictive of emotional and behavioural difficulties in children with refractory focal epilepsy." Thesis, Royal Holloway, University of London, 2014. http://digirep.rhul.ac.uk/items/e1e081c7-3d68-8a76-2a43-b294e1dd7dad/1/.

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Focal epilepsy in childhood is associated with increased risk for developing behavioral, emotional, cognitive and social–adaptive impairments. The present thesis focused on mental health difficulties in paediatric refractory focal epilepsy. It undertook a detailed evaluation of the predictive power of several demographic (gender, age at assessment), clinical (age at onset and duration of epilepsy, seizure frequency), localization (lobe and lateralization of pathology) and cognitive variables (performance in intellectual, memory and academic attainment measures) for mood, conduct, inattention/hyperactivity and peer relationship difficulties, as assessed by parental report. Data from a population of 282 children and adolescents, previously collected for clinical purposes, were examined, using a series of univariate and multivariate analyses. Mental health difficulties were found to be highly prevalent, with peer relationships the most frequently reported area of difficulty, followed by inattention/hyperactivity and emotional difficulties. Different patterns of associations between the variables examined here and individual emotional/behavioural difficulties were revealed, partially confirming and extending previous findings in the literature. Longer duration of epilepsy was found to increase the risk for developing emotional difficulties; male gender and earlier age at onset the risk for conduct difficulties; male gender, earlier age at onset, longer duration and frontal lobe localization the risk for attention/hyperactivity difficulties; and finally longer duration, higher seizure frequency and right hemisphere lateralization the risk for peer difficulties. Lower cognitive functioning was found associated with overall increased mental health difficulties and a lower VIQ was predictive of all types of difficulties. Developing a firm understanding of the risk factors that contribute to mental health comorbidities in focal paediatric epilepsy can help identify and provide assessment and intervention to children who are at higher risk earlier, thus significantly improving quality of life.
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Chalasani, Trishala. "AUTOMATED ASSESSMENT FOR THE THERAPY SUCCESS OF FOREIGN ACCENT SYNDROME : Based on Emotional Temperature." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15330.

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Context. Foreign Accent Syndrome is a rare neurological disorder, where among other symptoms of the patient’s emotional speech is affected. As FAS is one of the mildest speech disorders, there has not been much research done on the cost-effective biomarkers which reflect recovery of competences speech. Objectives. In this pilot study, we implement the Emotional Temperature biomarker and check its validity for assessing the FAS. We compare the results of implemented biomarker with another biomarker based on the global distances for FAS and identify the better one. Methods. To reach the objective, the emotional speech data of two patients at different phases of the treatment are considered. After preprocessing, experiments are performed on various window sizes and the observed correctly classified instances in automatic recognition are used to calculate Emotional temperature. Further, we use the better biomarker for tracking the recovery in the patient’s speech. Results. The Emotional temperature of the patient is calculated and compared with the ground truth and with that of the other biomarker. The Emotional temperature is calculated to track the emergence of compensatory skills in speech. Conclusions. A biomarker based on the frame-view of speech signal has been implemented. The implementation has used the state of art feature set and thus is an unproved version of the classical Emotional Temperature. The biomarker has been used to automatically assess the recovery of two patients diagnosed with FAS. The biomarker has been compared against the global view biomarker and has advantages over it. It also has been compared to human evaluations and captures the same dynamics.
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Books on the topic "Predictive Mental Health Analytics"

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Mittal, Mamta, and Lalit Mohan Goyal. Predictive Analytics of Psychological Disorder on Health: Data Analytics on Psychology Disorder. Springer, 2022.

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Data Science and Predictive Analytics: Biomedical and Health Applications using R. Springer, 2018.

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Dinov, Ivo D. Data Science and Predictive Analytics: Biomedical and Health Applications using R. Springer, 2019.

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Dinov, Ivo D. Data Science and Predictive Analytics: Biomedical and Health Applications Using R. Springer International Publishing AG, 2022.

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Data Science and Predictive Analytics: Biomedical and Health Applications Using R. Springer International Publishing AG, 2024.

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Data Science and Predictive Analytics: Biomedical and Health Applications using R. Springer, 2018.

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Passos, Ives Cavalcante, Benson Mwangi, and Flávio Kapczinski. Personalized Psychiatry: Big Data Analytics in Mental Health. Springer International Publishing AG, 2019.

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Dekker, Kas, and Maarten Dijkstra. School Bullying: Predictive Factors, Coping Strategies and Effects on Mental Health. Nova Science Publishers, Incorporated, 2013.

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Hill, Thomas, Joseph Hilbe, Mitchell Goldstein, Linda Miner, and Pat Bolding. Practical Predictive Analytics and Decisioning Systems for Medicine: Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research. Elsevier Science & Technology Books, 2016.

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Nisbet, Robert, Gary D. Miner, Mitchell Goldstein, Linda A. Miner, and Nephi Walton. Practical Predictive Analytics and Decisioning Systems for Medicine: Informatics Accuracy and Cost-Effectiveness for Healthcare Administration and Delivery Including Medical Research. Elsevier Science & Technology Books, 2014.

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Book chapters on the topic "Predictive Mental Health Analytics"

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Nag, Akash, Maddhuja Sen, and Jyotiraditya Saha. "Integration of Predictive Analytics and Cloud Computing for Mental Health Prediction." In Predictive Analytics in Cloud, Fog, and Edge Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18034-7_8.

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Deshpande, Mrinmayee, Pradnya Mehta, Nilesh Sable, Utkarsha Baraskar, Ishika Ingole, and Vaishnavi Shinde. "Mental Health Prediction Using Artificial Intelligence." In Data Management, Analytics and Innovation. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3245-6_4.

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Mallick, Sumitra, and Mrutyunjaya Panda. "Predicting Mental Health Disorders in the Technical Workplace: A Study on Feature Selection and Classification Algorithms." In Data Management, Analytics and Innovation. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3242-5_13.

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Wu, Yu, Qiuyu Ji, Ameng Zhao, Hong Li, and Yan Zhang. "The Construction of Mental Health Prediction Model Based on Data Mining Technology." In 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7466-2_11.

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Hilty, Donald M., Yang Cheng, and David D. Luxton. "Artificial Intelligence and Predictive Modeling in Mental Health." In Digital Mental Health. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-59936-1_13.

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Weng, Wei-Hung. "Machine Learning for Clinical Predictive Analytics." In Leveraging Data Science for Global Health. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47994-7_12.

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Rehman, Fazal, M. Lakshmi, K. Aditya Shastry, Syed Ismail, and Wasif Irshad. "Predictive Analysis Model for Mental Health." In Computational Vision and Bio-Inspired Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9573-5_54.

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Cullen, Theresa, and Jean E. Garcia. "Data Mining, Data Analytics, and Bioinformatics." In Innovations in Global Mental Health. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-70134-9_141-1.

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Cullen, Theresa, and Jean E. Garcia. "Data Mining, Data Analytics, and Bioinformatics." In Innovations in Global Mental Health. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57296-9_141.

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Rizwan, Muhammad, Nasib Zaman, Abdur Rauf, et al. "Predictive Analysis of Psychological Disorders on Health." In Predictive Analytics of Psychological Disorders in Healthcare. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1724-0_1.

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Conference papers on the topic "Predictive Mental Health Analytics"

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Mohanraj, S., R. Ramesh Krishna, M. Shan Adams, and C. Nallusamy. "Machine Learning for Mental Health Prediction From Social Media Activity." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961560.

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Liu, Dong. "Optimizing Mental Health Status Prediction Models Using Machine Learning Algorithms." In 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI). IEEE, 2024. https://doi.org/10.1109/icdacai65086.2024.00097.

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Padmakala, S., and S. T. Gopukumar. "Predictive Analytics in Mental Health: Machine Learning Models for Major Depressive Disorder Detection using Sensor Data." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10940218.

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Vats, Prashant, Pravin R. Kshirsagar, Kamal Upreti, Keshav Lalit, Tan Kuan Tak, and Shubham Mahajan. "Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges." In 2025 International Conference on Intelligent Control, Computing and Communications (IC3). IEEE, 2025. https://doi.org/10.1109/ic363308.2025.10957310.

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Al-Farouni, Mohammed, J. Jeyasudha, V. Navya Sree, R. Venkatasubramanian, and A. Ameelia Roseline. "Predictive Analytics for Mental Health Crises using Social Media Data with Attention Mechanism based Support Vector Machine Classification." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796269.

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Bhuvanya, R., Heblin Berscilla, Rohith, K. Kishore Kumar, and T. Kujani. "Predictive Analytics for Improved Fetal Health Management." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866318.

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Barkhade, Jayesh, Sahil Jagtap, Gayatri Shahare, Kamlesh Kalbande, Pooja Yerunkar, and Prasheel Thakre. "AI BOT: Mental Health Detection and Counselling." In 2024 International Conference on Big Data Analytics in Bioinformatics (DABCon). IEEE, 2024. https://doi.org/10.1109/dabcon63472.2024.10919413.

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Singh, Gurnimarjit, and Kanwarpartap Singh Gill. "Mental Health Analytics: A Comparative Exploration of Machine Learning Models." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837048.

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Beri, Mohit, Kanwarpartap Singh Gill, Deepak Upadhyay, and Swati Devliyal. "AI Insights: Revolutionizing Mental Health Assessments Through Predictive Models." In 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT). IEEE, 2024. http://dx.doi.org/10.1109/ic2sdt62152.2024.10696042.

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Ozsahin, Dilber Uzun, Declan Ikechukwu Emegano, Leena R. David, Abir Jaafar Hussain, Berna Uzun, and Ilker Ozsahin. "Machine Learning-Based Predictive Modeling of Mental Health Comorbidities." In 2024 17th International Conference on Development in eSystem Engineering (DeSE). IEEE, 2024. https://doi.org/10.1109/dese63988.2024.10911997.

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Reports on the topic "Predictive Mental Health Analytics"

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Ritter, Judith. A preliminary investigation of the predictive and evaluative capacity of the PARS scale in a community mental health clinic. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2150.

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Pasupuleti, Murali Krishna. AI and Quantum-Nano Frontiers: Innovations in Health, Sustainability, Energy, and Security. National Education Services, 2025. https://doi.org/10.62311/nesx/rr525.

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Abstract: This research report explores transformative advancements at the intersection of Artificial Intelligence (AI), Quantum Computing, and Nanotechnology, focusing on breakthrough innovations in health, sustainability, energy, and global security. By integrating quantum algorithms, AI-driven analytics, and advanced nanomaterials, this report highlights revolutionary solutions in precision medicine, predictive diagnostics, sustainable energy storage, universal water purification, and cybersecurity. Real-world case studies and emerging technologies such as graphene-based nanomaterials, quantum-enhanced drug discovery, smart microgrids, and quantum cryptography demonstrate how interdisciplinary integration accelerates global progress. Finally, ethical frameworks, strategic recommendations, and future roadmaps are provided to guide responsible deployment of these transformative technologies for widespread societal benefit. Keywords: Artificial Intelligence, Quantum Computing, Nanotechnology, Precision Medicine, Renewable Energy, Sustainability, Graphene, Smart Microgrids, Quantum Cryptography, Cybersecurity, Neuromorphic Computing, Water Purification, Ethical Implications, Global Security, Interdisciplinary Research.
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Rahman, Kazi, Grace Lee, Kristina Vine, Amba-Rose Atkinson, Michael Tong, and Veronica Matthews. Impacts of climate change on health and health services in northern New South Wales: an Evidence Check rapid review. The Sax Institute, 2022. http://dx.doi.org/10.57022/xlsj7564.

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This rapid review investigated the effects of climate change on health and health services in northern NSW—a known ‘hotspot’ for natural disasters—over the next 10-20 years. It included 92 peer-reviewed articles and 9 grey literature documents, with 17% focused on Northern NSW. Climate change will cause both an increase in average temperatures and in extreme weather events and natural disasters. Impacts particularly affecting Northern NSW are expected to include increases and exacerbations of: mental illness; infectious diseases, including those transmitted by mosquitoes, water and food; heat-related illnesses; chronic diseases including respiratory and cardiac conditions; injuries; and mortality—with vulnerable groups being most affected. Demand for health services will increase, but there will also be disruptions to medication supply and service availability. A whole-of-system approach will be needed to address these issues. There are numerous gaps in the research evidence and a lack of predictive modelling and robust locally relevant data.
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Papadopulos, Anastacia. The Prevalence and Predictive Nature of Victimization, Substance Abuse and Mental Health on Recidivism: A Comparative Longitudinal Examination of Male and Female Oregon Department of Corrections Inmates. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.204.

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Professor Tamsin Ford CBE – ‘Supporting children’s mental health as schools re-open’. ACAMH, 2020. http://dx.doi.org/10.13056/acamh.12491.

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Slides and transcript available. This was a live webinar recorded on Wednesday 8 July 2020 for ACAMH West Midlands Branch. ACAMH members can now receive a CPD certificate for watching this recorded lecture. Simply email membership@acamh.org with the day and time you watch it, so we can check the analytics, and we'll email you your certificate.
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