ReviewFrontiers in artificial intelligence2022
A Unified Framework on Generalizability of Clinical Prediction Models.
Review in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.Diagnostic and prognostic research · 2026Review
- Integration of artificial intelligence applications in clinical pharmacy services: A scoping review.Future healthcare journal · 2026Review
- Perioperative predictors of biochemical remission in acromegaly: a predictive modeling study.Pituitary · 2026Article
- Ordinal Clinical Outcome Modeling with Temporal Validation to Support Hospital Capacity Planning During Acute Infectious Disease Burden.International journal of environmental research and public health · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Reframing precision nutrition in irritable bowel syndrome: a mechanism-informed conceptual framework for responder prediction and clinical translation.Frontiers in immunology · 2026Review
- Prediction models for stunting at 2-years-old from Indonesian newborn population.BMC pediatrics · 2025Article
- Cochrane corner: artificial intelligence for diagnosing exudative age-related macular degeneration.Eye (London, England) · 2025Article
- Development and validation of a dynamic early warning system with time-varying machine learning models for predicting hemodynamic instability in critical care: a multicohort study.Critical care (London, England) · 2025Article
- Heterogeneity in mortality risk prediction: a study of vulnerable adults in the Canadian longitudinal study on aging.Aging clinical and experimental research · 2025Article
- Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective Observational Study by Using the Common Data Model.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a nomogram based on LASSO-logistic regression for predicting carotid atherosclerosis in patients with hypertension.Frontiers in cardiovascular medicine · 2025Article
- Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.Journal of medical Internet research · 2024Article
- Spatial-temporal attention for video-based assessment of intraoperative surgical skill.Scientific reports · 2024Article
- Perspectives on validation of clinical predictive algorithms.NPJ digital medicine · 2023Article
- Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
To be useful, clinical prediction models (CPMs) must be generalizable to patients in new settings. Evaluating generalizability of CPMs helps identify spurious relationships in data, provides insights on when they fail, and thus, improves the explainability of the CPMs. There are discontinuities in concepts related to generalizability of CPMs in the clinical research and machine learning domains. Specifically, conventional statistical reasons to explain poor generalizability such as inadequate model development for the purposes of generalizability, differences in coding of predictors and outcome between development and external datasets, measurement error, inability to measure some predictors, and missing data, all have differing and often complementary treatments, in the two domains. Much of the current machine learning literature on generalizability of CPMs is in terms of dataset shift of which several types have been described. However, little research exists to synthesize concepts in the two domains. Bridging this conceptual discontinuity in the context of CPMs can facilitate systematic development of CPMs and evaluation of their sensitivity to factors that affect generalizability. We survey generalizability and dataset shift in CPMs from both the clinical research and machine learning perspectives, and describe a unifying framework to analyze generalizability of CPMs and to explain their sensitivity to factors affecting it. Our framework leads to a set of signaling statements that can be used to characterize differences between datasets in terms of factors that affect generalizability of the CPMs.
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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.