ArticleJournal of the American Medical Informatics Association : JAMIA2016
Real-time prediction of mortality, readmission, and length of stay using electronic health record data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis.Frontiers in medicine · 2023Pooled it
- Earlier medically ready for discharge date entry associated with lower unnecessary bed days.BMC health services research · 2026Article
- Article
- Performance fairness of neural network models in early risk assessment of inpatients with varying severity: a retrospective study.BMC medical informatics and decision making · 2026Article
- Concurrent prediction of in-hospital mortality and length of stay using single-task, multiclass and multitask machine learning.BMJ digital health & AI · 2025Article
- De-identification is not enough: a comparison between de-identified and synthetic clinical notes.Scientific reports · 2024Article
- Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study.Journal of medical Internet research · 2024Article
- Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: machine learning model development and evaluation.BMC medical informatics and decision making · 2024Article
- In-hospital mortality, readmission, and prolonged length of stay risk prediction leveraging historical electronic patient records.JAMIA open · 2024Article
- On the evaluation of synthetic longitudinal electronic health records.BMC medical research methodology · 2024Article
- Accuracy of machine learning to predict the outcomes of shoulder arthroplasty: a systematic review.Arthroplasty (London, England) · 2024Article
- Predicting the Length of Stay of Cardiac Patients Based on Pre-Operative Variables-Bayesian Models vs. Machine Learning Models.Healthcare (Basel, Switzerland) · 2024Article
- BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Artificial Intelligence in Health, Health Care, and Biomedical Science: An AI Code of Conduct Principles and Commitments Discussion Draft.NAM perspectives · 2024Article
- Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables.Sensors (Basel, Switzerland) · 2023Article
- Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach.JMIR medical informatics · 2022Article
- Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- A systematic review of the prediction of hospital length of stay: Towards a unified framework.PLOS digital health · 2022Article
- A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Domain Adaptation Using Convolutional Autoencoder and Gradient Boosting for Adverse Events Prediction in the Intensive Care Unit.Frontiers in artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs). MATERIALS AND
methodsA Bayesian Network model was built to estimate the probability of a hospitalized patient being "at home," in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32 634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years.
resultsThe model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model's predictive ability was highest within 24 hours from prediction (AUROC = 0.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84. DISCUSSION: We developed the first non-disease-specific model that simultaneously predicts remaining days of hospitalization, death, and readmission as part of the same outcome. By providing a future daily probability for each outcome class, we enable the visualization of future patient trajectories. Among these, it is possible to identify trajectories indicating expected discharge, expected continuing hospitalization, expected death, and possible readmission.
conclusionsBayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.
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Registered trials
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.