PMID- 30066653
OWN - NLM
STAT- MEDLINE
DCOM- 20190627
LR  - 20190627
IS  - 1472-6947 (Electronic)
IS  - 1472-6947 (Linking)
VI  - 18
IP  - Suppl 2
DP  - 2018 Jul 23
TI  - Discovering and identifying New York heart association classification from 
      electronic health records.
PG  - 48
LID - 10.1186/s12911-018-0625-7 [doi]
LID - 48
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.
FAU - Zhang, Rui
AU  - Zhang R
AD  - Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA. 
      zhan1386@umn.edu.
AD  - College of Pharmacy, University of Minnesota, Minneapolis, MN, USA. 
      zhan1386@umn.edu.
FAU - Ma, Sisi
AU  - Ma S
AD  - Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
AD  - Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
FAU - Shanahan, Liesa
AU  - Shanahan L
AD  - Medtronic, Inc., Minneapolis, MN, USA.
FAU - Munroe, Jessica
AU  - Munroe J
AD  - Medtronic, Inc., Minneapolis, MN, USA.
FAU - Horn, Sarah
AU  - Horn S
AD  - Medtronic, Inc., Minneapolis, MN, USA.
FAU - Speedie, Stuart
AU  - Speedie S
AD  - Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20180723
PL  - England
TA  - BMC Med Inform Decis Mak
JT  - BMC medical informatics and decision making
JID - 101088682
SB  - IM
MH  - Aged
MH  - Cardiac Resynchronization Therapy
MH  - Disease Progression
MH  - *Electronic Health Records
MH  - Female
MH  - Heart Failure/*classification
MH  - Humans
MH  - Machine Learning
MH  - Male
MH  - Middle Aged
MH  - *Natural Language Processing
MH  - New York
MH  - Treatment Outcome
PMC - PMC6069768
OTO - NOTNLM
OT  - Clinical notes
OT  - Electronic health records
OT  - Natural language processing
OT  - New York heart association (NYHA)
COIS- AUTHORS' INFORMATION: Described in the title page. ETHICS APPROVAL AND CONSENT TO 
      PARTICIPATE: Ethics approval for this study was obtained from the University of 
      Minnesota Institutional Review Board (IRB). All patients were included based on 
      their consent forms. COMPETING INTERESTS: The authors declare that they have no 
      competing interests. PUBLISHER'S NOTE: Springer Nature remains neutral with 
      regard to jurisdictional claims in published maps and institutional affiliations.
EDAT- 2018/08/02 06:00
MHDA- 2019/06/30 06:00
PMCR- 2018/07/23
CRDT- 2018/08/02 06:00
PHST- 2018/08/02 06:00 [entrez]
PHST- 2018/08/02 06:00 [pubmed]
PHST- 2019/06/30 06:00 [medline]
PHST- 2018/07/23 00:00 [pmc-release]
AID - 10.1186/s12911-018-0625-7 [pii]
AID - 625 [pii]
AID - 10.1186/s12911-018-0625-7 [doi]
PST - epublish
SO  - BMC Med Inform Decis Mak. 2018 Jul 23;18(Suppl 2):48. doi: 
      10.1186/s12911-018-0625-7.