NDLI: Early detection of heart failure with varying prediction windows by structured and unstructured data in electronic health records

Content Provider	IEEE Xplore Digital Library
Author	Yajuan Wang Ng, K. Byrd, R.J. Jianying Hu Ebadollahi, S. Daar, Z. deFilippi, C. Steinhubl, S.R. Stewart, W.F.
Copyright Year	2015
Description	Author affiliation: Scripps Health, San Diego, CA, USA (Steinhubl, S.R.) \|\| Sch. of Med., Univ. of Maryland, Baltimore, MD, USA (deFilippi, C.) \|\| Geisinger Health Syst., Danville, PA, USA (Daar, Z.) \|\| IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA (Yajuan Wang; Ng, K.; Byrd, R.J.; Jianying Hu; Ebadollahi, S.) \|\| Sutter Health Res., Walnut Creek, CA, USA (Stewart, W.F.)
Abstract	Heart failure (HF) prevalence is increasing and is among the most costly diseases to society. Early detection of HF would provide the means to test lifestyle and pharmacologic interventions that may slow disease progression and improve patient outcomes. This study used structured and unstructured data from electronic health records (EHR) to predict onset of HF with a particular focus on how prediction accuracy varied in relation to time before diagnosis. EHR data were extracted from a single health care system and used to identify incident HF among primary care patients who received care between 2001 and 2010. A total of 1,684 incident HF cases were identified and 13,525 controls were selected from the same primary care practices. Models were compared by varying the beginning of the prediction window from 60 to 720 days before HF diagnosis. As the prediction window decreased, the performance [AUC (95% CIs)] of the predictive HF models increased from 65% (63%-66%) to 74% (73%-75%) for the unstructured, from 73% (72%-75%) to 81% (80%-83%) for the structured, and from 76% (74%-77%) to 83% (77%-85%) for the combined data.
Starting Page	2530
Ending Page	2533
File Size	770013
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318907
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hafnium Predictive models Heart Diseases Feature extraction Electronic medical records Medical diagnostic imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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