Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.

ABSTRACT:

Objectives

To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes.

Methods

An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms-on a training set of 127 axSpA cases and 423 non-cases-and unsupervised algorithms to identify patients with high probability of havi

SUBMITTER: Zhao SS 

PROVIDER: S-EPMC7850056 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature