SynthesisJournal of the American Medical Informatics Association : JAMIA2023
Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 5 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- Electronic health records: managerial insights from an umbrella review.BMC health services research · 2026Pooled it
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Machine learning models for adverse drug reaction prediction. A systematic review and three-level multilevel meta-analysis.Frontiers in pharmacology · 2026Pooled it
- Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis.The Journal of international medical research · 2024Pooled it
- A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.Age and ageing · 2024Pooled it
- AI for Health Care Quality and Patient Safety: Scoping Review of Diagnostic, Predictive, and Decision Support Applications.Journal of medical Internet research · 2026Article
- Evaluating the Performance of Traditional Pharmacoepidemiologic and Machine Learning Models to Predict Pregnancies at Risk of Major Congenital Malformations.Birth defects research · 2026Article
- Prediction tools to prioritise hospitalised adult patients at risk of drug related problems: An umbrella review.PLOS digital health · 2026Article
- Development and validation of a machine learning-based clinical decision support tool for stratifying intravenous medication risk in hospitalized patients with heart failure.International journal of clinical pharmacy · 2026Article
- Machine learning risk prediction models for medication harm in hospitalised adult patients.Therapeutic advances in drug safety · 2026Article
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- Towards a prescribing monitoring system for medication safety evaluation within electronic health records: a scoping review.BMC medical informatics and decision making · 2025Article
- Challenges in detecting and predicting adverse drug events via distributed analysis of electronic health record data from German university hospitals.PLOS digital health · 2025Article
- Reimagining clinical AI: from clickstreams to clinical insights with EHR use metadata.npj health systems · 2025Review
- Artificial intelligence in clinical decision support and the prediction of adverse events.Frontiers in digital health · 2025Review
- Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.JAMIA open · 2024Article
- KG-LIME: predicting individualized risk of adverse drug events for multiple sclerosis disease-modifying therapy.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Digital patient twins for personalized therapeutics and pharmaceutical manufacturing.Frontiers in digital health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients. MATERIALS AND
methodsWe searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS).
resultsTwenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated.
conclusionsSeveral challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.
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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.