NDLI: Predicting atrial fibrillation and flutter using Electronic Health Records

Content Provider	IEEE Xplore Digital Library
Author	Karnik, S. Sin Lam Tan Berg, B. Glurich, I. Jinfeng Zhang Vidaillet, H.J. Page, C.D. Chowdhary, R.
Copyright Year	2012
Description	Author affiliation: Med. Sch., Dept. of Biostat. & Med. Inf., Univ. of Wisconsin, Madison, WI, USA (Berg, B.; Page, C.D.) \|\| Dept. of Stat., Florida State Univ., Tallahassee, FL, USA (Jinfeng Zhang) \|\| Biomed. Inf. Res. Center, Marshfield Clinic Res. Found., Marshfield, WI, USA (Karnik, S.; Sin Lam Tan; Glurich, I.; Vidaillet, H.J.; Chowdhary, R.)
Abstract	Electronic Health Records (EHR) contain large amounts of useful information that could potentially be used for building models for predicting onset of diseases. In this study, we have investigated the use of free-text and coded data in Marshfield Clinic's EHR, individually and in combination for building machine learning based models to predict the first ever episode of atrial fibrillation and/or atrial flutter (AFF). We trained and evaluated our AFF models on the EHR data across different time intervals (1, 3, 5 and all years) prior to first documented onset of AFF. We applied several machine learning methods, including naïve bayes, support vector machines (SVM), logistic regression and random forests for building AFF prediction models and evaluated these using 10-fold cross-validation approach. On text-based datasets, the best model achieved an F-measure of 60.1%, when applied exclusively to coded data. The combination of textual and coded data achieved comparable performance. The study results attest to the relative merit of utilizing textual data to complement the use of coded data for disease onset prediction modeling.
Sponsorship	IEEE Eng. Medicine Biol. Soc.
Starting Page	5562
Ending Page	5565
File Size	564904
Page Count	4
File Format	PDF
ISBN	9781424441198
ISSN	1557170X
e-ISBN	9781457717871
DOI	10.1109/EMBC.2012.6347254
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Atrial fibrillation Data models Unified modeling language Diseases Support vector machines Medical diagnostic imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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