ArticleJournal of the American Medical Informatics Association : JAMIA2017
Congestive heart failure information extraction framework for automated treatment performance measures assessment.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.
- A review on knowledge and information extraction from PDF documents and storage approaches.Frontiers in artificial intelligence · 2025Pooled it
- Natural Language Processing Algorithm to Extract Multiple Myeloma Stage From Oncology Notes in the Veterans Affairs Healthcare System.JCO clinical cancer informatics · 2024Article
- Accurately assessing congenital heart disease using artificial intelligence.PeerJ. Computer science · 2024Article
- A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports.Biomimetics (Basel, Switzerland) · 2023Article
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
- A hierarchical multilabel graph attention network method to predict the deterioration paths of chronic hepatitis B patients.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Evaluating Patients' Experiences with Healthcare Services: Extracting Domain and Language-Specific Information from Free-Text Narratives.International journal of environmental research and public health · 2022Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment.JAMIA open · 2019Article
- Quality Measures in Heart Failure: the Past, the Present, and the Future.Current heart failure reports · 2019Review
- Extraction of Ejection Fraction from Echocardiography Notes for Constructing a Cohort of Patients having Heart Failure with reduced Ejection Fraction (HFrEF).Journal of medical systems · 2018Article
- Underestimated prevalence of heart failure in hospital inpatients: a comparison of ICD codes and discharge letter information.Clinical research in cardiology : official journal of the German Cardiac Society · 2018Article
- Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs.JMIR medical informatics · 2018Article
- Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- From Sour Grapes to Low-Hanging Fruit: A Case Study Demonstrating a Practical Strategy for Natural Language Processing Portability.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018Article
- Clinical information extraction applications: A literature review.Journal of biomedical informatics · 2018Review
- Developing a cardiovascular disease risk factor annotated corpus of Chinese electronic medical records.BMC medical informatics and decision making · 2017Article
- Making Sense of Big Textual Data for Health Care: Findings from the Section on Clinical Natural Language Processing.Yearbook of medical informatics · 2017Review
- Unlocking echocardiogram measurements for heart disease research through natural language processing.BMC cardiovascular disorders · 2017Observational
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThis paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system - CHIEF - developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration project aiming at improving the detection of patients not receiving recommended care for CHF.
designCHIEF is based on the Apache Unstructured Information Management Architecture framework, and uses a combination of rules, dictionaries, and machine learning methods to extract left ventricular function mentions and values, CHF medications, and documented reasons for a patient not receiving these medications. MEASUREMENTS: The training and evaluation of CHIEF were based on subsets of a reference standard of various clinical notes from 1083 Veterans Health Administration patients. Domain experts manually annotated these notes to create our reference standard. Metrics used included recall, precision, and the F 1 -measure.
resultsIn general, CHIEF extracted CHF medications with high recall (>0.990) and good precision (0.960-0.978). Mentions of Left Ventricular Ejection Fraction were also extracted with high recall (0.978-0.986) and precision (0.986-0.994), and quantitative values of Left Ventricular Ejection Fraction were found with 0.910-0.945 recall and with high precision (0.939-0.976). Reasons for not prescribing CHF medications were more difficult to extract, only reaching fair accuracy with about 0.310-0.400 recall and 0.250-0.320 precision.
conclusionThis study demonstrated that applying natural language processing to unlock the rich and detailed clinical information found in clinical narrative text notes makes fast and scalable quality improvement approaches possible, eventually improving management and outpatient treatment of patients suffering from CHF.
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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 OpenQuestion graph.