ArticleScientific reports2022
Towards interpretable, medically grounded, EMR-based risk prediction models.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.GeroScience · 2026Pooled it
- An Evaluation of Clinician and Informatician Perspectives on Medication Harm Risk Prediction Tools for Use in a Tertiary Hospital Setting.Journal of evaluation in clinical practice · 2026Article
- A data and knowledge cross-level fusion-driven learning framework for detecting missing diagnosis.NPJ digital medicine · 2026Article
- Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help.JMIR AI · 2026Article
- Exploring fibroblast activation protein as an early biomarker in chronic lung allograft dysfunction.The European respiratory journal · 2026Article
- Reimagining kidney value-based care: leveraging data science for dynamic, clinician-level risk prediction.Current opinion in nephrology and hypertension · 2026Review
- Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.BMC medicine · 2026Article
- Foundation Model-Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi-Institutional Retrospective Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
- Molecular subtype identification and prognosis stratification based on golgi apparatus-related genes in head and neck squamous cell carcinoma.BMC medical genomics · 2024Article
- Article
- The impact of COVID-19 pandemic on ridesourcing services differed between small towns and large cities.PloS one · 2022Article
- The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review.Digital healthArticle
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine-learning based risk prediction models have the potential to improve patient outcomes by assessing risk more accurately than clinicians. Significant additional value lies in these models providing feedback about the factors that amplify an individual patient's risk. Identification of risk factors enables more informed decisions on interventions to mitigate or ameliorate modifiable factors. For these reasons, risk prediction models must be explainable and grounded on medical knowledge. Current machine learning-based risk prediction models are frequently 'black-box' models whose inner workings cannot be understood easily, making it difficult to define risk drivers. Since machine learning models follow patterns in the data rather than looking for medically relevant relationships, possible risk factors identified by these models do not necessarily translate into actionable insights for clinicians. Here, we use the example of risk assessment for postoperative complications to demonstrate how explainable and medically grounded risk prediction models can be developed. Pre- and postoperative risk prediction models are trained based on clinically relevant inputs extracted from electronic medical record data. We show that these models have similar predictive performance as models that incorporate a wider range of inputs and explain the models' decision-making process by visualizing how different model inputs and their values affect the models' predictions.
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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.