Feature extraction for phenotyping from semantic and knowledge resources.

ABSTRACT:

Objective

Phenotyping algorithms can efficiently and accurately identify patients with a specific disease phenotype and construct electronic health records (EHR)-based cohorts for subsequent clinical or genomic studies. Previous studies have introduced unsupervised EHR-based feature selection methods that yielded algorithms with high accuracy. However, those selection methods still require expert intervention to tweak the parameter settings according to the EHR data distribution for each phenotype. To further accelerate the development of phenotyping algorithms, we propose a fully automated and robust unsupervised feature selection method that leverages only publicly available medical knowledge sources, instead of EHR data.

Methods

SEmantics-Driven Feature Extraction (SEDFE)

SUBMITTER: Ning W 

PROVIDER: S-EPMC6424621 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature