ArticleJournal of the American Medical Informatics Association : JAMIA2020
Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 6 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Prediction Models for In-Hospital Delirium Using Routinely Collected Electronic Health Record Data: Systematic Review.JMIR medical informatics · 2026Pooled it
- Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.Journal of medical Internet research · 2024Pooled it
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Risk of bias in prognostic models of hospital-induced delirium for medical-surgical units: A systematic review.PloS one · 2023Pooled it
- Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Prediction of delirium in trauma patients using interpretable machine learning.Scientific reports · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Relevance and Feasibility of a "Geriatric Delirium Pass" for Older Patients with Elective Surgeries: Findings from a Multi-Methods Study.Geriatrics (Basel, Switzerland) · 2026Article
- Early prediction of postoperative delirium in major surgery patients using multimodal preoperative EHR and medical notes: a retrospective cohort study in the Indiana network for patient care.BMJ public health · 2026Article
- Implementation and Updating of Clinical Prediction Models: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- SURGE-ahead postoperative delirium prediction: external validation and open-source library.European geriatric medicine · 2025Article
- Machine Learning Multimodal Model for Delirium Risk Stratification.JAMA network open · 2025Article
- Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model.JMIR medical informatics · 2025Article
- Enhancing Delirium Prediction and Prevention in Elderly Patients Through Machine Learning-Based Analysis.Sultan Qaboos University medical journal · 2025Article
- Longitudinal Model Shifts of Machine Learning-Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals.Journal of medical Internet research · 2024Article
- [Geriatric assessment goes digital-Possible applications in the emergency department setting].Zeitschrift fur Gerontologie und Geriatrie · 2024Review
- Machine learning-based delirium prediction in surgical in-patients: a prospective validation study.JAMIA open · 2024Article
- Clinical evaluation of a machine learning-based dysphagia risk prediction tool.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.Scientific reports · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveMachine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting. MATERIALS AND
methodsDelirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting.
resultsDuring clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded (r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry. DISCUSSION: The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals.
conclusionsOur study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.