Relevant bibliographies by topics / Predictive Mental Health Analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictive Mental Health Analytics'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 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, 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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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 fu
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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 developmen
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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
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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 principl
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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. Resu
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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, though
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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 intel
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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/h
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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 objecti
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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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Zim, Md Khadimul Islam, and Md Abu Hanif. "Predictive Analytics in Mental Health: AI for Identifying Risk Factors and Preventing Crises." In 2025 6th International Conference on Recent Advances in Information Technology (RAIT). IEEE, 2025. https://doi.org/10.1109/rait65068.2025.11088892.

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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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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, quan
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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-
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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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