Discovering and identifying New York heart association classification from electronic health records.
Background: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure (HF) patients in an electronic health recor...
| Published in: | BMC Medical Informatics & Decision Making Vol. 18; no. 2 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
7/23/2018 Supplement2
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130871050&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130871050 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 7/23/2018 Supplement2 vid: 18 iid: 2 pid: 24147 pub: BioMed Central artinfo: ui: 130871050 130871050 NLM30066653 130871050 10.1186/s12911-018-0625-7 NLM30066653 130871050 ppct: 1 formats: tig: atl: Discovering and identifying New York heart association classification from electronic health records. aug: au: Zhang, Rui Ma, Sisi Shanahan, Liesa Munroe, Jessica Horn, Sarah Speedie, Stuart affil: Institute for Health Informatics University of Minnesota Minneapolis MN USA sug: subj: Natural Language Processing Heart Failure Classification Human Disease Progression New York Middle Age Cardiac Resynchronization Therapy Female Treatment Outcomes Aged Male Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Background: Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) class is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure (HF) patients in an electronic health record (EHR) consistently, over time, can provide better understanding of the progression of heart failure and assessment of CRT response and effectiveness. Though NYHA is rarely stored in EHR structured data, such information is often documented in unstructured clinical notes.Methods: We accessed HF patients' data in a local EHR system and identified potential sources of NYHA, including local diagnosis codes, procedures, and clinical notes. We further investigated and compared the performances of rule-based versus machine learning-based natural language processing (NLP) methods to identify NYHA class from clinical notes.Results: Of the 36,276 patients with a diagnosis of HF or a CRT implant, 19.2% had NYHA class mentioned at least once in their EHR. While NYHA class existed in descriptive fields association with diagnosis codes (31%) or procedure codes (2%), the richest source of NYHA class was clinical notes (95%). A total of 6174 clinical notes were matched with hospital-specific custom NYHA class diagnosis codes. Machine learning-based methods outperformed a rule-based method. The best machine-learning method was a random forest with n-gram features (F-measure: 93.78%).Conclusions: NYHA class is documented in different parts in EHR for HF patients and the documentation rate is lower than expected. NLP methods are a feasible way to extract NYHA class information from clinical notes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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