ArticleJMIR medical informatics2024
BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study.
Article in JMIR medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing for geriatric syndromes: a systematic review of methods, applications, and challenges.BMC medical informatics and decision making · 2026Pooled it
- Geriatric syndromes extraction from discharge summaries: a new dataset, annotation scheme and initial findings.Frontiers in digital health · 2026Article
- Article
- Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.BMJ health & care informatics · 2025Article
- Performance of Natural Language Processing versus International Classification of Diseases Codes in Building Registries for Patients With Fall Injury: Retrospective Analysis.JMIR medical informatics · 2025Article
- Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study.JMIR medical informatics · 2025Article
- Article
- Article
- Nurses' Perception towards Electronic Medical Records System: An Integrative Review of Barriers and Facilitators.Iranian journal of public health · 2025Review
- Validation of large language models for detecting pathologic complete response in breast cancer using population-based pathology reports.BMC medical informatics and decision making · 2024Article
- Article
- Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives.medRxiv : the preprint server for health sciences · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundInpatient falls are a substantial concern for health care providers and are associated with negative outcomes for patients. Automated detection of falls using machine learning (ML) algorithms may aid in improving patient safety and reducing the occurrence of falls.
objectiveThis study aims to develop and evaluate an ML algorithm for inpatient fall detection using multidisciplinary progress record notes and a pretrained Bidirectional Encoder Representation from Transformers (BERT) language model.
methodsA cohort of 4323 adult patients admitted to 3 acute care hospitals in Calgary, Alberta, Canada from 2016 to 2021 were randomly sampled. Trained reviewers determined falls from patient charts, which were linked to electronic medical records and administrative data. The BERT-based language model was pretrained on clinical notes, and a fall detection algorithm was developed based on a neural network binary classification architecture.
resultsTo address various use scenarios, we developed 3 different Alberta hospital notes-specific BERT models: a high sensitivity model (sensitivity 97.7, IQR 87.7-99.9), a high positive predictive value model (positive predictive value 85.7, IQR 57.2-98.2), and the high F
conclusionsThe developed algorithm provides an automated and accurate method for inpatient fall detection using multidisciplinary progress record notes and a pretrained BERT language model. This method could be implemented in clinical practice to improve patient safety and reduce the occurrence of falls in hospitals.
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