ArticleNature protocols2021
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.
Article in Nature protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Article
- Reference intervals reimagined with IRIS for earlier detection and better disease monitoring.Scientific reports · 2026Article
- 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
- Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.PLOS digital health · 2026Article
- Augmenting Electronic Health Records for Adverse Event Detection.medRxiv : the preprint server for health sciences · 2026Article
- Advancing precision triage in strangulated small bowel obstruction: from static scores to dynamic multiparametric models.Frontiers in surgery · 2026Review
- Beyond the diagnostic threshold: paradigm shift in laboratory medicine from passive reaction to predictive capability.Frontiers in medicine · 2026Article
- Construction of a Machine Learning-Based Risk Prediction Model for Drug-Induced Acute Kidney Injury in Elderly Patients.International journal of nephrology and renovascular disease · 2026Article
- Challenges and Recommendations for Electronic Health Records Data Extraction and Preparation for Dynamic Prediction Modeling in Hospitalized Patients: Practical Guide and Tutorial.Journal of medical Internet research · 2025Article
- Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis.Scientific reports · 2025Observational
- Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models.Findings of ACL. NAACL · 2025Article
- Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025Article
- Development and validation of a real-time prediction model for acute kidney injury in hospitalized patients.Nature communications · 2025Article
- Advances in Language-Model-Informed Protein-Nucleic Acid Binding Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Distilling the knowledge from large-language model for health event prediction.Scientific reports · 2024Article
- Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.EClinicalMedicine · 2024Article
- Machine learning in infectious diseases: potential applications and limitations.Annals of medicine · 2024Review
- Privacy-preserving large language models for structured medical information retrieval.NPJ digital medicine · 2024Article
- Derivation and Validation of a Machine Learning Model for the Prevention of Unplanned Dialysis.Clinical journal of the American Society of Nephrology : CJASN · 2024Article
Corrections and comments
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Authors and funding
31 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks.
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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.