SureLDA: A multidisease automated phenotyping method for the electronic health record.

ABSTRACT:

Objective

A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes.

Materials and methods

Surrogate-guided ensemble latent Dirichlet allocation (sureLDA) is a label-free multidimensional phenotyping method. It first uses the PheNorm

SUBMITTER: Ahuja Y 

PROVIDER: S-EPMC7481024 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature