Surrogate-assisted feature extraction for high-throughput phenotyping.

ABSTRACT:

Objective

Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy.

Methods

The proposed Surrogate-Assisted Feature Extraction (SAFE) method selects candidate features from a pool of comprehensive medical concepts found in publicly available knowledge sources. The target phenotype's International Classification of Diseases, Ninth Revision and natural language processin

SUBMITTER: Yu S 

PROVIDER: S-EPMC6080726 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature