Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record.

ABSTRACT:

Background

Systemic sclerosis (SSc) is a rare disease with studies limited by small sample sizes. Electronic health records (EHRs) represent a powerful tool to study patients with rare diseases such as SSc, but validated methods are needed. We developed and validated EHR-based algorithms that incorporate billing codes and clinical data to identify SSc patients in the EHR.

Methods

We used a de-identified EHR with over 3 million subjects and identified 1899 potential SSc subjects with at least 1 count of the SSc ICD-9 (710.1) or ICD-10-CM (M34*) codes. We randomly selected 200 as a training set for chart review. A subject was a case if diagnosed with SSc by a rheumatologist, dermatologist, or pulmonologist. We selected the following algorithm components based on clinical knowl

SUBMITTER: Jamian L 

PROVIDER: S-EPMC6937803 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature