ArticleScientific reports2021
Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed, 96 citations in OpenAlex.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation.IEEE transactions on bio-medical engineering · 2026Review
- Toward Drift-Aware Multimodal Early Warning for Acute Exacerbations of Chronic Obstructive Pulmonary Disease.JMIR medical informatics · 2026Article
- Simulation of covariate and concept drift in machine learning hospital admission prediction from emergency triage.NPJ digital medicine · 2026Article
- Clinical-ShiftEval: a framework for simulating and evaluating model adaptation in dynamic clinical NLP tasks.BMC medical informatics and decision making · 2026Article
- State of the AI: Post-Deployment Monitoring of Radiology-Focused Internally Developed AI.Mayo Clinic proceedings. Digital health · 2026Review
- How Do Radiologists Currently Monitor AI in Radiology and What Challenges Do They Face? An Interview Study and Qualitative Analysis.Journal of imaging informatics in medicine · 2026Article
- Understanding and Addressing Bias in Artificial Intelligence Systems: A Primer for the Emergency Medicine Physician.Journal of the American College of Emergency Physicians open · 2026Article
- How data drift impacts the safety and interpretability of machine learning models predicting risk from blood glucose control.BMJ digital health & AI · 2026Article
- Atherogenic index of plasma and risk of acute kidney injury in critically ill patients: a multi-cohort study with machine learning and SHAP analysis.Renal failure · 2025Article
- AI-RiskX: An Explainable Deep Learning Approach for Identifying At-Risk Patients During Pandemics.Bioengineering (Basel, Switzerland) · 2025Article
- Explainability in the age of large language models for healthcare.Communications engineering · 2025Article
- A Comprehensive Drift-Adaptive Framework for Sustaining Model Performance in COVID-19 Detection From Dynamic Cough Audio Data: Model Development and Validation.Journal of medical Internet research · 2025Article
- Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI Models.JAMA network open · 2025Article
- Clinicians must participate in the development of multimodal AI.EClinicalMedicine · 2025Review
- Beyond Benchmarks: Evaluating Generalist Medical Artificial Intelligence With Psychometrics.Journal of medical Internet research · 2025Article
- Predicting onward care needs at admission to reduce discharge delay using explainable machine learning.Scientific reports · 2025Article
- One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics.Scientific reports · 2025Article
- Predicting Inpatient Admissions From Emergency Department Triage Using Machine Learning: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- The urgency of centering safety-net organizations in AI governance.NPJ digital medicine · 2025Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model's performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.
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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.