ArticleNPJ digital medicine2021
Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
81 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.Nature communications · 2024Pooled 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
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- Leveraging artificial intelligence to optimize neuromodulation in the treatment of patients with chronic pain.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Artificial intelligence in multidisciplinary tumor boards enhancing decision making and clinical outcomes in oncology.iScience · 2025Review
- Integrating a host biomarker with a large language model for diagnosis of lower respiratory tract infection.Nature communications · 2025Article
- Multiple sclerosis risk stratification and healthcare cost prediction using machine learning.Communications medicine · 2025Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Equipping mathematical models for hospital dynamics using information theory.NPJ digital medicine · 2025Article
- Prediction of prolonged length of stay from first 2 days of hospitalization data: a SHAP value-based variable selection method for a simplified model.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2025Article
- Artificial Intelligence in Medication Management for Older Adults in Low- and Middle-Income Countries: A Narrative Review.Aging medicine (Milton (N.S.W)) · 2025Review
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Machine learning can help clinicians to make individualized patient predictions only if researchers demonstrate models that contribute novel insights, rather than learning the most likely next step in a set of actions a clinician will take. We trained deep learning models using only clinician-initiated, administrative data for 42.9 million admissions using three subsets of data: demographic data only, demographic data and information available at admission, and the previous data plus charges recorded during the first day of admission. Models trained on charges during the first day of admission achieve performance close to published full EMR-based benchmarks for inpatient outcomes: inhospital mortality (0.89 AUC), prolonged length of stay (0.82 AUC), and 30-day readmission rate (0.71 AUC). Similar performance between models trained with only clinician-initiated data and those trained with full EMR data purporting to include information about patient state and physiology should raise concern in the deployment of these models. Furthermore, these models exhibited significant declines in performance when evaluated over only myocardial infarction (MI) patients relative to models trained over MI patients alone, highlighting the importance of physician diagnosis in the prognostic performance of these models. These results provide a benchmark for predictive accuracy trained only on prior clinical actions and indicate that models with similar performance may derive their signal by looking over clinician's shoulders-using clinical behavior as the expression of preexisting intuition and suspicion to generate a prediction. For models to guide clinicians in individual decisions, performance exceeding these benchmarks is necessary.
Identifiers
What OpenQuestion holds
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.