Article

Development and Evaluation of Machine Learning Models to Predict Mechanical Restraint and Related Coercive Measures in Hospital Psychiatry

2025-12-16

Abstract excerpt

<h4>Introduction</h4> Use of coercive measures in psychiatric hospitals is clinically and ethically challenging. Aiming to support prevention, we developed and evaluated machine learning models to predict both mechanical restraint and a broader composite outcome that includes related coercive measures. <h4>Methods</h4> The dataset comprised electronic health records (EHR) from adults (≥18 years) who had at least...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 7605a2d1-eadb-591d-80f6-4cdf5de7f04a
DOI: 10.64898/2025.12.15.25342272

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Development and Evaluation of Machine Learning Models to Predict Mechanical Restraint and Related Coercive Measures in Hospital PsychiatryDOI 10.64898/2025.12.15.25342272Read

Absolute and relative risk of mechanical restraint, forced medication, and seclusion during involuntary psychiatric hospitalisation: a population-wide cohort studySemantic proximity Read

Validation of the Immobility Harm Risk Score for Predicting Inpatient Immobility-Associated Outcomes: A Retrospective Cohort StudySemantic proximity Read

Prevalence and Risk Factors for Seclusion and Restraint in Old-age Psychiatry Inpatient UnitsSemantic proximity Read

Predicting Intentional Self-Harm Following Psychiatric Discharge in Catalonia, Spain: Machine Learning Models from Linked Registry DataSemantic proximity Read

SBDH-Reader: an LLM-powered method for extracting social and behavioral determinants of health from clinical notesSemantic proximity Read

Physical Restraint Use in a US Intensive Care Setting – Protocol for a retrospective cross-sectional analysis using the MIMIC-IV repositorySemantic proximity Read

A tool to evaluate proportionality and necessity in the use of restrictive practices in forensic mental health settings: The DRILL tool (Dundrum Restriction, Intrusion and Liberty Ladders).Semantic proximity Read

Predicting Hospitalization following Psychiatric Crisis Care using Machine LearningSemantic proximity Read

COVID-19 Inmate Risk Appraisal (CIRA): Development and validation of a screening tool to assess COVID-19 vulnerability in prisonsSemantic proximity Read

Predicting Self-harm Incidents Following Inpatient Visits using Disease Comorbidity NetworkSemantic proximity Read

A Local Outpatient Practice-Level Prediction Model for Short-Term Psychiatric Emergency PresentationSemantic proximity Read

Identifying Key Predictive Features for Opioid Use Disorder Using Machine LearningSemantic proximity Read