ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Understanding Heart Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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.
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Who cites it
27 citing papers in PubMed, 36 citations in OpenAlex.
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- An explainable machine learning framework for cardiovascular risk prediction using structured health data.Frontiers in artificial intelligence · 2026Article
- A Machine-Learning-Based Clinical Decision Model for Predicting Amputation Risk in Patients with Diabetic Foot Ulcers: Diagnostic Performance and Practical Implications.Diagnostics (Basel, Switzerland) · 2025Article
- Online machine learning model for predicting delirium risk in elderly patients with chronic kidney disease: development and preliminary validation.European journal of medical research · 2025Article
- Developing a machine learning-based prediction model for postinduction hypotension.Journal of clinical monitoring and computing · 2025Article
- Explainable AI reveals tissue pathology and psychosocial drivers of opioid prescription for non-specific chronic low back pain.Scientific reports · 2025Article
- A Machine Learning-Based Guide for Repeated Laboratory Testing in Pediatric Emergency Departments.Diagnostics (Basel, Switzerland) · 2025Article
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- Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events.PloS one · 2025Article
- Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.Frontiers in endocrinology · 2025Article
- Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.BMC medical research methodology · 2024Article
- Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Machine Learning Approach to ClassifyInfection and drug resistance · 2024Article
- Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study.Quantitative imaging in medicine and surgery · 2023Article
- Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach.Healthcare informatics research · 2023Article
- A hybrid system to understand the relations between assessments and plans in progress notes.Journal of biomedical informatics · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjusting therapies are critical for improving patient outcomes. An experienced cardiologist can make accurate HF stage diagnoses based on combination of symptoms, signs, and lab results from the electronic health records (EHR) of a patient, without directly measuring heart function. We examined whether machine learning models, more specifically the XGBoost model, can accurately predict patient stage based on EHR, and we further applied the SHapley Additive exPlanations (SHAP) framework to identify informative features and their interpretations. Our results indicate that based on structured data from EHR, our models could predict patients' ejection fraction (EF) scores with moderate accuracy. SHAP analyses identified informative features and revealed potential clinical subtypes of HF. Our findings provide insights on how to design computing systems to accurately monitor disease progression of HF patients through continuously mining patients' EHR data.
Indexed as
Identifiers
35308970PMC8861751W3138058568What 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.