ArticleScientific reports2022
Data-driven identification of heart failure disease states and progression pathways using electronic health records.
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 15 papers, 1 of them a synthesis 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.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data-driven decision making in patient management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
- Early emergency department decision support for heart failure hospitalization using triage-level unstructured and structured data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Implementation of Machine Learning in Heart Failure Trials.Current heart failure reports · 2026Review
- Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records.Journal of healthcare informatics research · 2026Article
- Article
- 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
- Identifying time patterns in Huntington's disease trajectories using dynamic time warping-based clustering on multi-modal data.Scientific reports · 2025Article
- Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Article
- Robust extraction of pneumonia-associated clinical states from electronic health records.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- An open-source framework for end-to-end analysis of electronic health record data.Nature medicine · 2024Article
- Evolution of economic burden of heart failure by ejection fraction in newly diagnosed patients in Spain.BMC health services research · 2023Article
- Is the patient speaking or the nurse? Automatic speaker type identification in patient-nurse audio recordings.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart failure (HF) is a leading cause of morbidity, healthcare costs, and mortality. Guideline based segmentation of HF into distinct subtypes is coarse and unlikely to reflect the heterogeneity of etiologies and disease trajectories of patients. While analyses of electronic health records show promise in expanding our understanding of complex syndromes like HF in an evidence-driven way, limitations in data quality have presented challenges for large-scale EHR-based insight generation and decision-making. We present a hypothesis-free approach to generating real-world characteristics and progression patterns of HF. Patient disease state snapshots are extracted from the complaints mentioned in unstructured clinical notes. Typical disease states are generated by clustering and characterized in terms of their distinguishing features, temporal relationships, and risk of important clinical events. Our analysis generates a comprehensive "disease phenome" of real-world patients computed from large, noisy, secondary-use EHR datasets created in a routine clinical setting.
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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 Socratic graph.