Automatic methods to extract New York heart association classification from clinical notes

Rui Zhang, Sisi Ma, Liesa Shanahan, Jessica Munroe, Sarah Horn, Stuart Speedie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) classification is often used as a measure of a patient's response to CRT. Identifying NYHA class for heart failure 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. However, NYHA is rarely stored in EHR structured data such information is often documented in unstructured clinical notes. In this study, we thus investigated the use of natural language processing (NLP) methods to identify NYHA classification from clinical notes. We collected 6,174 clinical notes that were matched with hospital-specific custom NYHA class diagnosis codes. Machine-learning based methods performed similar with a rule-based method. The best machine-learning method, support vector machine with n-gram features, performed the best (93% F-measure). Further validation of the findings is required.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
EditorsIllhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1296-1299
Number of pages4
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume2017-January

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/TerritoryUnited States
CityKansas City
Period11/13/1711/16/17

Bibliographical note

Funding Information:
Research supported by Medtronic through a contract with the University of Minnesota and by the Clinical and Translational Science Institute grant from National Center for Advancing Translational Sciences of the National Institutes

Funding Information:
Research supported by Medtronic through a contract with the University of Minnesota and by the Clinical and Translational Science Institute grant from National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR000114, Blazar)

Funding Information:
J. Munroe, L. Shanahan and S. Horn are employees of Medtronic, Inc. that manufactures and markets Cardiac Resynchronization Therapy devices. The research reported in this article was performed in collaboration with the named Medtronic, Inc. employees by R. Zhang, S. Ma and S. Speedie, faculty members at the University of Minnesota under a contract with the University of Minnesota funded by Medtronic, Inc. The University of Minnesota researchers did not receive any additional compensation for their work.

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Clinical Notes
  • Electronic Health Records
  • Natural Language Processing
  • New York Heart Association (NYHA)

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