ArticleInternational journal of medical informatics2014
Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records.
Article in International journal of medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 4 of them syntheses that pooled it.
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Who cites it
49 citing papers in PubMed, 4 syntheses or guidelines pooled it, 133 citations in OpenAlex.
- 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
- Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature.BMC medical informatics and decision making · 2017Pooled it
- Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.Yearbook of medical informatics · 2015Pooled it
- Scoping review of return to work in patients with heart failure.Heart failure reviews · 2026Article
- NLP-ROPCare: predicting retinopathy of prematurity with admission notes using natural language processing.BMJ open ophthalmology · 2026Observational
- Article
- Improvement initiatives in the diagnostic process of heart failure: a scoping review.Frontiers in cardiovascular medicine · 2025Review
- Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.BMC medical research methodology · 2024Article
- Initial development of tools to identify child abuse and neglect in pediatric primary care.BMC medical informatics and decision making · 2023Article
- Trends in Incidence of Chronic Heart Failure in Patients With Rheumatoid Arthritis: A Population-Based Study Validating Different Heart Failure Definitions.The Journal of rheumatology · 2023Article
- Physicians' Perspectives on a Multi-Dimensional Model for the Roles of Electronic Health Records in Approaching a Proper Differential Diagnosis.Journal of personalized medicine · 2023Article
- Combining text mining with clinical decision support in clinical practice: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Spanish version of the Heart Failure Somatic Perception Scale (HFSPS v.3) - psychometric properties.Frontiers in cardiovascular medicine · 2023Article
- Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes.JCO clinical cancer informatics · 2022Article
- Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.Heart failure clinics · 2022Review
- Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care.Journal of general internal medicine · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Health-Related Quality of Life Among Heart Failure Patients Attending an Outpatient Clinic in the University of Gondar Comprehensive Specialized Hospital Northwest, Ethiopia, 2020: Using Structural Equation Modeling Approach.Patient related outcome measures · 2021Article
- Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes.BMC medical informatics and decision making · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveEarly detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF.
designWe developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown. MEASUREMENTS: Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling.
resultsOur criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932.
conclusionOur system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.
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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.