{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1929"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1929","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Developing a predictive model for factors related to risk of aggression in psychiatric inpatients using physiological and clinical data","abstract":"Unpredictable, aggressive behavior in psychiatric inpatients remains a challenge in mental health, emphasizing the negative impact on both patients and staff. With estimates suggesting a significant percentage of patients exhibiting aggression during psychiatric stays, the study employs big data analysis on an actual clinical data set extracted from patients’ medical records to develop a predictive model for clinical aggression risk factors. Retrospective analysis covers variables such as heart rate, blood pressure, age, incidents, medication, hospitalization, suicide risk, education, type of incident, and gender. Statistical analyses, including t-test, stepwise regression, and logistic regression, reveal a significant correlation between a history of aggression and a lower resting heart rate. The final model identifies predictors such as systolic and diastolic blood pressure, medication refusal, and gender. The study highlights the potential of big data in enhancing medical insights and recommends future exploration of streaming and temporal data for more precise disease prevention.","abstract_html":"Unpredictable, aggressive behavior in psychiatric inpatients remains a challenge in mental health, emphasizing the negative impact on both patients and staff. With estimates suggesting a significant percentage of patients exhibiting aggression during psychiatric stays, the study employs big data analysis on an actual clinical data set extracted from patients’ medical records to develop a predictive model for clinical aggression risk factors. Retrospective analysis covers variables such as heart rate, blood pressure, age, incidents, medication, hospitalization, suicide risk, education, type of incident, and gender. Statistical analyses, including t-test, stepwise regression, and logistic regression, reveal a significant correlation between a history of aggression and a lower resting heart rate. The final model identifies predictors such as systolic and diastolic blood pressure, medication refusal, and gender. The study highlights the potential of big data in enhancing medical insights and recommends future exploration of streaming and temporal data for more precise disease prevention.","abstract_has_math":false,"creators":["Farsi, Leila"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Health Sciences (MHSc)","degree_level":null,"degree_discipline":"Health Informatics","degree_department":null,"school":null,"contributors":[],"advisors":["McGregor, Carolyn"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-01","date_published":"2025-02-01","updated_at":"2026-07-24T05:35:43Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1929","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["McGregor, Carolyn"]},{"key":"dc:creator","label":"Author","values":["Farsi, Leila"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-29T17:09:10Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-29T17:09:10Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-02-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Health Informatics"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Health Sciences (MHSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1929"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Unpredictable, aggressive behavior in psychiatric inpatients remains a challenge in mental health, emphasizing the negative impact on both patients and staff. With estimates suggesting a significant percentage of patients exhibiting aggression during psychiatric stays, the study employs big data analysis on an actual clinical data set extracted from patients’ medical records to develop a predictive model for clinical aggression risk factors. Retrospective analysis covers variables such as heart rate, blood pressure, age, incidents, medication, hospitalization, suicide risk, education, type of incident, and gender. Statistical analyses, including t-test, stepwise regression, and logistic regression, reveal a significant correlation between a history of aggression and a lower resting heart rate. The final model identifies predictors such as systolic and diastolic blood pressure, medication refusal, and gender. The study highlights the potential of big data in enhancing medical insights and recommends future exploration of streaming and temporal data for more precise disease prevention."]},{"key":"dc:title","label":"Title","values":["Developing a predictive model for factors related to risk of aggression in psychiatric inpatients using physiological and clinical data"]}]}],"canonical_facts":{"dc:contributor.advisor":["McGregor, Carolyn"],"dc:creator":["Farsi, Leila"],"dc:date.accessioned":["2025-04-29T17:09:10Z"],"dc:date.available":["2025-04-29T17:09:10Z"],"dc:date.issued":["2025-02-01"],"dc:description.abstract":["Unpredictable, aggressive behavior in psychiatric inpatients remains a challenge in mental health, emphasizing the negative impact on both patients and staff. With estimates suggesting a significant percentage of patients exhibiting aggression during psychiatric stays, the study employs big data analysis on an actual clinical data set extracted from patients’ medical records to develop a predictive model for clinical aggression risk factors. Retrospective analysis covers variables such as heart rate, blood pressure, age, incidents, medication, hospitalization, suicide risk, education, type of incident, and gender. Statistical analyses, including t-test, stepwise regression, and logistic regression, reveal a significant correlation between a history of aggression and a lower resting heart rate. The final model identifies predictors such as systolic and diastolic blood pressure, medication refusal, and gender. The study highlights the potential of big data in enhancing medical insights and recommends future exploration of streaming and temporal data for more precise disease prevention."],"dc:identifier.uri":["https://hdl.handle.net/10155/1929"],"dc:language.iso":["en"],"dc:title":["Developing a predictive model for factors related to risk of aggression in psychiatric inpatients using physiological and clinical data"],"dc:type":["Thesis"],"thesis:degree_discipline":["Health Informatics"],"thesis:degree_name":["Master of Health Sciences (MHSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:43Z"}