Learning statistical models of phenotypes using noisy labeled training data | Zendy

Vibhu Agarwal | Zendy; Tanya Podchiyska | Zendy; Juan M. Banda | Zendy; Veena Goel | Zendy; Tiffany I. Leung | Zendy; Evan Minty | Zendy; Timothy E. Sweeney | Zendy; Elsie Gyang | Zendy; Nigam H. Shah | Zendy

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Learning statistical models of phenotypes using noisy labeled training data

Author(s) -

Vibhu Agarwal,

Tanya Podchiyska,

Juan M. Banda,

Veena Goel,

Tiffany I. Leung,

Evan Minty,

Timothy E. Sweeney,

Elsie Gyang,

Nigam H. Shah

Publication year - 2016

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocw028

Subject(s) - computer science , scalability , machine learning , artificial intelligence , logistic regression , feature (linguistics) , phenotype , feature engineering , implementation , predictive modelling , data mining , deep learning , database , linguistics , philosophy , biochemistry , chemistry , gene , programming language

Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.

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