Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data.
Background: Falls are the most common hospital-acquired adverse event, leading to extended hospitalization, loss of independence, disability, and premature death. Routine fall risk assessments are time-consuming, even with limited factors. An AI-derived fall prediction model can provide more compreh...
| Publicado en: | Nursing Reports Vol. 16; no. 8; pp. 283 - 306 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=196616853&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 196616853 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2039439X EGNT jtl: Nursing Reports issn: 2039439X maglogo: N pubinfo: dt: Aug2026 vid: 16 iid: 8 pid: 97109 pub: MDPI artinfo: ui: 196616853 196616853 196616853 10.3390/nursrep16080283 196616853 ppf: 283 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data. aug: au: Mordiffi, Siti Zubaidah Guo, Xiujuan Goh, Mien Li Ngiam, Kee Yuan Wong, Neng Wei Chua, Jenny Furqan, Mohammad Shaheryar Chew, Han Shi Jocelyn affil: Nursing Department, National University Hospital, National University Health System, Singapore 119074, Singapore sug: subj: Machine Learning Models, Theoretical Accidental Falls Risk Factors Electronic Health Records Hospitalization Prediction Models Funding Source Human Male Female Adult Middle Age Aged Descriptive Statistics Validation Studies Artificial Intelligence Decision Trees Adverse Health Care Event Nonexperimental Studies Retrospective Design Record Review Logistic Regression Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Background: Falls are the most common hospital-acquired adverse event, leading to extended hospitalization, loss of independence, disability, and premature death. Routine fall risk assessments are time-consuming, even with limited factors. An AI-derived fall prediction model can provide more comprehensive and comparably accurate risk predictions quickly and as often as needed. Objective: To develop and validate a fall prediction model for fall risk in adult inpatients. Methods: Patient records from 2016 were extracted from the adult inpatient database, including information from the Electronic Inpatient Medication Records, SAP, and Hospital Incident Reporting System. The sample consisted of 1506 cases (1:5 faller to non-faller). The fall prediction model was trained using the following four variables: demographics, diagnosis, medications, and surgery. Data sources included the hospital's data repository, integrating admission/discharge, pharmacy, laboratory, and incident reports. Results: The support vector machine model performed best among all tested models, achieving an AUC of 0.803, recall of 0.816, and precision of 0.440. In the validation cohort (978 patients: 163 fallers and 815 non-fallers), the fall prediction model demonstrated moderate-to-good discrimination (AUC 0.79), with accuracy of 0.67, sensitivity of 0.46, and specificity of 0.86. Compared with the nursing four-item fall risk assessment, which showed lower discrimination (AUC 0.65, accuracy 0.65, sensitivity 0.58, specificity 0.72), the fall prediction model had better specificity and overall discrimination, though the nursing tool was more sensitive in identifying fallers. Conclusions: The fall prediction model using demographics, diagnoses, medication, and surgery data predicts falls risk effectively. It enables timely, accurate risk assessments and supports preventive interventions, saving nurses' time for direct patient care. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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