ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2020
Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Identification of hypertrophic cardiomyopathy on electrocardiographic images with deep learning.Nature cardiovascular research · 2025Article
- Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.medRxiv : the preprint server for health sciences · 2025Article
- Machine Learning Computer Vision Point of Care Decision Support of Echocardiographic Identification of Hypertrophic Cardiomyopathy.JACC. Advances · 2025Article
- HCM-Echo-VAR-Ensemble: Deep Ensemble Fusion to Detect Hypertrophic Cardiomyopathy in Echocardiograms.IEEE open journal of engineering in medicine and biology · 2025Article
- Hypertrophic Cardiomyopathy with Special Focus on Mavacamten and Its Future in Cardiology.Biomedicines · 2024Review
- From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions.Proceedings of machine learning research · 2024Article
- Machine-learning-based identification of patients with IgA nephropathy using a computerized medical billing database.PloS one · 2024Article
- Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach.JMIR medical informatics · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden cardiac death (SCD) in young adults. Despite the well-known risk factors and existing clinical practice guidelines, HCM patients are underdiagnosed and sub-optimally managed. Developing machine learning models on electronic health record (EHR) data can help in better diagnosis of HCM and thus improve hundreds of patient lives. Automated phenotyping using HCM billing codes has received limited attention in the literature with a small number of prior publications. In this paper, we propose a novel predictive model that helps physicians in making diagnostic decisions, by means of information learned from historical data of similar patients. We assembled a cohort of 11,562 patients with known or suspected HCM who have visited Mayo Clinic between the years 1995 to 2019. All existing billing codes of these patients were extracted from the EHR data warehouse. Target ground truth labeling for training the machine learning model was provided by confirmed HCM diagnosis using the gold standard imaging tests for HCM diagnosis echocardiography (echo), or cardiac magnetic resonance (CMR) imaging. As the result, patients were labeled into three categories of "yes definite HCM", "no HCM phenotype", and "possible HCM" after a manual review of medical records and imaging tests. In this study, a random forest was adopted to investigate the predictive performance of billing codes for the identification of HCM patients due to its practical application and expected accuracy in a wide range of use cases. Our model performed well in finding patients with "yes definite", "possible" and "no" HCM with an accuracy of 71%, weighted recall of 70%, the precision of 75%, and weighted F1 score of 72%. Furthermore, we provided visualizations based on multidimensional scaling and the principal component analysis to provide insights for clinicians' interpretation. This model can be used for the identification of HCM patients using their EHR data, and help clinicians in their diagnosis decision making.
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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.