alexa Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
Medicine

Medicine

General Medicine: Open Access

Author(s): Carroll RJ, Eyler AE, Denny JC

Abstract Share this page

Abstract Electronic Health Records (EHRs) provide a real-world patient cohort for clinical and genomic research. Phenotype identification using informatics algorithms has been shown to replicate known genetic associations found in clinical trials and observational cohorts. However, development of accurate phenotype identification methods can be challenging, requiring significant time and effort. We applied Support Vector Machines (SVMs) to both naïve (i.e., non-curated) and expert-defined collections of EHR features to identify Rheumatoid Arthritis cases using billing codes, medication exposures, and natural language processing-derived concepts. SVMs trained on naïve and expert-defined data outperformed an existing deterministic algorithm; the best performing naïve system had precision of 0.94 and recall of 0.87, compared to precision of 0.75 and recall of 0.51 for the deterministic algorithm. We show that with an expert defined feature set as few as 50-100 training samples are required. This study demonstrates that SVMs operating on non-curated sets of attributes can accurately identify cases from an EHR.
This article was published in AMIA Annu Symp Proc and referenced in General Medicine: Open Access

Relevant Expert PPTs

Relevant Speaker PPTs

  • Manish Kumar
    GOD’s diagnostic tool is the mathematics and prescription is physics
    PPT Version | PDF Version
  • Yi-Cheng Hu
    Detection of a negative correlation between prescription of Chinese herbal products containing coumestrol, genistein or daidzein and risk of subsequent endometrial cancer among tamoxifentreated female breast cancer survivors in Taiwan between 1998 and 2008: A population-based study
    PPT Version | PDF Version
  • Nihal Taskiran
    Investigation of Nursing Students Opinions Related to Their Pharmacology Knowledge Levels
    PPT Version | PDF Version

Recommended Conferences

Relevant Topics

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords