ArticleScientific reports2020
Multiscale classification of heart failure phenotypes by unsupervised clustering of unstructured electronic medical record data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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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.
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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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review.Current heart failure reports · 2023Pooled it
- Identifying Risk Groups in 73,000 Patients with Diabetes Receiving Total Hip Replacement: A Machine Learning Clustering Analysis.Journal of personalized medicine · 2025Article
- Clinical and research applications of natural language processing for heart failure.Heart failure reviews · 2025Review
- Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.JMIR medical informatics · 2025Article
- A Traumatic Brain Injury Prescreening Tool for Intimate Partner Violence Patients Using Initial Clinical Reports and Machine Learning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York.International journal of medical informatics · 2024Article
- Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Analysis of Prevalence and Clinical Features of Aortic Stenosis in Patients with and without Bicuspid Aortic Valve Using Machine Learning Methods.Journal of personalized medicine · 2023Article
- Seasonality of acute kidney injury phenotypes in England: an unsupervised machine learning classification study of electronic health records.BMC nephrology · 2023Article
- A network medicine approach to study comorbidities in heart failure with preserved ejection fraction.BMC medicine · 2023Article
- Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging.European journal of nuclear medicine and molecular imaging · 2023Article
- A distributable German clinical corpus containing cardiovascular clinical routine doctor's letters.Scientific data · 2023Article
- Changes in cardiac acoustic biomarkers before and after cardiac events in a patient with right-sided heart failure due to cor pulmonale.Journal of cardiology cases · 2023Article
- 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
- Moving beyond Table 1: A critical review of the literature addressing social determinants of health in chronic condition symptom cluster research.Nursing inquiry · 2023Review
- Incidence and impact of atrial fibrillation in heart failure patients: real-world data in a large community.ESC heart failure · 2022Article
- Predictors of Unrelieved Symptoms in All of Us Research Program Participants With Chronic Conditions.Journal of pain and symptom management · 2022Article
- Data-driven identification of heart failure disease states and progression pathways using electronic health records.Scientific reports · 2022Article
- Systematic approach to outcome assessment from coded electronic healthcare records in the DaRe2THINK NHS-embedded randomized trialEuropean heart journal. Digital health · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a leading cause of death and morbidity, heart failure (HF) is responsible for a large portion of healthcare and disability costs worldwide. Current approaches to define specific HF subpopulations may fail to account for the diversity of etiologies, comorbidities, and factors driving disease progression, and therefore have limited value for clinical decision making and development of novel therapies. Here we present a novel and data-driven approach to understand and characterize the real-world manifestation of HF by clustering disease and symptom-related clinical concepts (complaints) captured from unstructured electronic health record clinical notes. We used natural language processing to construct vectorized representations of patient complaints followed by clustering to group HF patients by similarity of complaint vectors. We then identified complaints that were significantly enriched within each cluster using statistical testing. Breaking the HF population into groups of similar patients revealed a clinically interpretable hierarchy of subgroups characterized by similar HF manifestation. Importantly, our methodology revealed well-known etiologies, risk factors, and comorbid conditions of HF (including ischemic heart disease, aortic valve disease, atrial fibrillation, congenital heart disease, various cardiomyopathies, obesity, hypertension, diabetes, and chronic kidney disease) and yielded additional insights into the details of each HF subgroup's clinical manifestation of HF. Our approach is entirely hypothesis free and can therefore be readily applied for discovery of novel insights in alternative diseases or patient populations.
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