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

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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2

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Bremer, Vincent [Verfasser], Burkhardt [Akademischer Betreuer] Funk, Burkhardt [Gutachter] Funk, Heleen [Gutachter] Riper, and Peter [Gutachter] Niemeyer. "Supporting therapy success by developing predictive models in e-mental-health / Vincent Bremer ; Gutachter: Burkhardt Funk, Heleen Riper, Peter Niemeyer ; Betreuer: Burkhardt Funk." Lüneburg : Leuphana Universität Lüneburg, 2021. http://d-nb.info/1241329842/34.

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12

Finch, Julie. "Examining the Impact of Psychological Capital on Student Mental Health and Wellbeing in an Australian School Context: Predictive Relationships and Outcomes of a Brief Novel Intervention." Thesis, Griffith University, 2022. http://hdl.handle.net/10072/416307.

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Psychological Capital (PsyCap) is a theory of human behaviour that refers to a suite of four positive psychological resources comprising hope, (self-) efficacy, resilience and optimism (HERO). PsyCap theory postulates that due to shared commonalities of the HERO constructs, omnibus PsyCap (i.e., combined HERO) more powerfully predicts a range of mental health, wellbeing and occupational outcomes than any individual component part. PsyCap theory and research developed within organisational psychology as a contextual construct associated with attitudes, behaviours and performance which has been

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13

Papadopulos, Anastacia Konstantinos. "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." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/204.

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As a consequence of increased awareness and the current scholarly debate regarding women's differential predictors of recidivism, criminal justice agencies are working with researchers in the field to expand their knowledge in this area. In 2007, Portland State University researchers in collaboration with the Oregon Department of Corrections conducted an investigation of factors emerging in the pathways and gender responsive literature as predictive of women's recidivism in a randomly selected sample of female (n=150) and male (n=150) inmates. This study used information gathered from that inv

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14

Nunn, Katherine Louise. "Investigation into risk assessment and staff coping with patient perpetrated violence in inpatient forensic psychiatric settings." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33090.

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The present thesis was carried out in part fulfilment of the Doctorate in Clinical Psychology at the University of Edinburgh. It is presented in portfolio format, comprising of two individual papers although a total thesis abstract provides an overview of the entire thesis. The first paper is a systematic review of existing empirical research. It explores the predictive validity of risk assessment tools for imminent (short-term) violence and aggression in forensic psychiatric settings. The second paper is an empirical study exploring how frontline nursing staff both predict and emotionally cop

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15

Khasawneh, Ahmad Ali. "GUIDELINES FOR COMPARING INTERVENTIONS, PREDICTING HIGH-RISK PATIENTS, AND CONDUCTING OPTIMIZATION FOR EARLY HF READMISSION." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1499011064948037.

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16

Santos, Maria Eugênia de Simone Brito dos. "Identificação de fatores associados à aderência ao tratamento, reinternação hospitalar e necessidades de cuidados de pacientes inseridos na rede pública de assistência em saúde mental." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/17/17148/tde-07012016-103706/.

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Na atual conjuntura assistencial em saúde mental, em que se prioriza modalidades de tratamento comunitário, o efetivo funcionamento da rede de assistência ao portador de transtorno mental é de vital importância. A região de Ribeirão Preto-SP, durante o processo de desinstitucionalização de pacientes com transtorno mental das últimas décadas, assistiu à diminuição substancial do número de leitos psiquiátricos e à criação de novos serviços comunitários, que mais recentemente parecem não ser suficientes para atender à demanda de internação existente. Nesse contexto, o objetivo deste estudo foi an

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17

Wang, Guanjin. "Health care predictive analytics using artificial intelligence techniques." Thesis, 2018. http://hdl.handle.net/10453/128014.

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University of Technology Sydney. Faculty of Engineering and Information Technology.<br>In recent years, advances in Artificial Intelligence (AI) are opening the door for intelligent health care data prediction and decision making. Machine learning, as an increasingly popular approach to AI, has been widely used to learn directly from data, adapt independently, and produce predictive outcomes, which support doctors when encountering complex health care predictive analytics. However, traditional machine learning methods are not always perfectly working in the health field, intrinsically due to l

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18

Brisimi, Theodora. "Centralized and distributed learning methods for predictive health analytics." Thesis, 2017. https://hdl.handle.net/2144/27007.

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The U.S. health care system is considered costly and highly inefficient, devoting substantial resources to the treatment of acute conditions in a hospital setting rather than focusing on prevention and keeping patients out of the hospital. The potential for cost savings is large; in the U.S. more than $30 billion are spent each year on hospitalizations deemed preventable, 31% of which is attributed to heart diseases and 20% to diabetes. Motivated by this, our work focuses on developing centralized and distributed learning methods to predict future heart- or diabetes- related hospitalizations b

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19

Alsinglawi, Belal. "Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models." Thesis, 2022. http://hdl.handle.net/1959.7/uws:67523.

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The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) resear

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20

Liu, De-Chian, and 劉德謙. "Real-time Intelligent Health Care Demand Forecasting based on Big Data Predictive Analytics - A Case Study." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jv69xj.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>107<br>With the evolution of the information and communication technology, following the development of Industry 4.0, Artificial Intelligence (AI), Big Data, and Cloud Computing play important roles in the smart manufacturing factory. In this thesis, we focused on the big data predictive analytics, having three properties: velocity, variety and volume in healthcare management. In this thesis, we proposed a real time intelligent medical forecasting system, which was divided into two phases. In the first phase, a Big Data approach for Medical Demand Forecasting, includ

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21

Kunjan, Kislaya. "A big data augmented analytics platform to operationalize efficiencies at community clinics." Diss., 2016. http://hdl.handle.net/1805/13388.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Community Health Centers (CHCs) play a pivotal role in delivery of primary healthcare to the underserved, yet have not benefited from a modern data analytics platform that can support clinical, operational and financial decision making across the continuum of care. This research is based on a systems redesign collaborative of seven CHC organizations spread across Indiana to improve efficiency and access to care. Three research questions (RQs) formed the basis of this research, each of which seeks to address known knowle

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22

"Predictive Validity of Select Scales of the MMPI-A on Adolescent Depression." Doctoral diss., 2010. http://hdl.handle.net/2286/R.I.8685.

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abstract: The purpose of this study was to evaluate whether five select scales of the MMPI-A (F, Scale 2, A-dep, A-lse, and A-aln) are predictive of a diagnosis of a major depressive episode according to the current DSM-IV-TR criteria. Participants were 90 girls and 58 boys in a clinical psychiatric setting. The study examined two separate hypotheses across the five scales. The first set of hypotheses tested whether a significant T-score on each of the five scales would predict a diagnosis of a major depressive episode in clinical adolescents. The second set of hypotheses attempted to step awa

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23

Roby, Sarah J. "Understanding Barriers to Enrollment and Completion of Evidence-based Interventions for Trauma Exposed Youth: the Potential Predictive Role of Parental Trauma Exposure." 2014. http://scholarworks.gsu.edu/iph_theses/319.

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Child trauma exposure (CTE) is an important public health concern in the U.S.; more than two-thirds of children report experiencing a traumatic event by the age of 16. CTE may have important acute and long-term physiological, developmental, behavioral, and psychological implications if not addressed. Trauma-focused cognitive behavioral therapy (TF-CBT) is the gold standard for treatment of child trauma and is well-supported for resulting in significant decreases in negative mental health outcomes associated with CTE. Despite the efficacy of evidence-based interventions such as TF-CBT, many chi

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24

Doctoroff, Greta L. "Evaluating externalizing behavior in preschoolers : the predictive utility of parent report, teacher report, and observation." 2001. https://scholarworks.umass.edu/theses/2378.

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25

Rocha, Maria Isabel Santos. "Avaliação epidemiológica dos internamentos através de um serviço de urgência de segunda linha (SII)." Master's thesis, 2015. http://hdl.handle.net/10362/14339.

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RESUMO: Introdução: A Comissão Nacional para a reestruturação dos Serviços de Saúde Mental em 2004, fez uma proposta de âmbito regional, ao nível da região de Saúde do Norte, levando a uma alteração da rede de referenciação hospitalar dos internamentos em psiquiatria. Métodos: Realizou-se um estudo epidemiológico longitudinal para avaliar factores preditivos de internamento através de um serviço de Urgência de Segunda Linha (SII), que de algum modo reflectisse o funcionamento dos serviços de internamento na região de Saúde do Norte, ao longo de 12 anos, nomeadamente relacionando com factores

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