ArticleNPJ digital medicine2021
The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards.
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 32 papers, 3 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
32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- Digital technologies and clinical decision support tools available for diabetes triage in the inpatient setting: A systematic literature review.Diabetic medicine : a journal of the British Diabetic Association · 2026Pooled it
- Sepsis mortality prediction using machine learning and deep learning - a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Early detection of sepsis using machine learning algorithms: a systematic review and network meta-analysis.Frontiers in medicine · 2024Pooled it
- Trial
- Dynamic Prediction of Day Mortality in Patients With Trauma Using a Hybrid Neural Network Model: Model Development and Evaluation Study.Journal of medical Internet research · 2026Article
- Anticipating health and care trajectories from routinely collected social care records.npj health systems · 2026Article
- Computable Phenotype for Identifying Undiagnosed Hypermobile Ehlers-Danlos Syndrome: Protocol for a Development and Validation Study.JMIR research protocols · 2026Article
- Development and validation of a machine learning-based model for predicting live birth outcomes in patients undergoing frozen-thawed embryo transfer.BMC pregnancy and childbirth · 2026Review
- The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study.Journal of medical Internet research · 2026Observational
- Towards reliable prediction of intraoperative hypotension: a cross-center evaluation of deep learning-based and MAP-derived methods.Journal of clinical monitoring and computing · 2026Article
- Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data.Journal of clinical medicine · 2026Article
- Progress in sepsis prediction models: from traditional scoring systems to multimodal intelligence and clinical translation.Frontiers in medicine · 2026Review
- Development and validation of machine learning nomograms for predicting mortality after cardiac valve surgery.Frontiers in medicine · 2026Article
- Evaluating the Machine Learning Models in Predicting Intensive Care Unit Discharge for Neurosurgical Patients Undergoing Craniotomy: A Big Data Analysis.Neurocritical care · 2025Article
- Development and validation of a real-time risk prediction model for acute kidney injury in hospitalized pediatric patients.World journal of pediatrics : WJP · 2025Article
- Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study.Journal of medical Internet research · 2025Article
- Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk Score.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.BMC medical research methodology · 2025Article
- Electronic Health Records-based identification of newly diagnosed Crohn's Disease cases.Artificial intelligence in medicine · 2025Article
- Exploring home healthcare clinicians' needs for using clinical decision support systems for early risk warning.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Problem framing is critical to developing risk prediction models because all subsequent development work and evaluation takes place within the context of how a problem has been framed and explicit documentation of framing choices makes it easier to compare evaluation metrics between published studies. In this work, we introduce the basic concepts of framing, including prediction windows, observation windows, window shifts and event-triggers for a prediction that strongly affects the risk of clinician fatigue caused by false positives. Building on this, we apply four different framing structures to the same generic dataset, using a sepsis risk prediction model as an example, and evaluate how framing affects model performance and learning. Our results show that an apparently good model with strong evaluation results in both discrimination and calibration is not necessarily clinically usable. Therefore, it is important to assess the results of objective evaluations within the context of more subjective evaluations of how a model is framed.
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.