Classifying Pseudogout Using Machine Learning Approaches With Electronic Health Record Data.

ABSTRACT:

Objective

Identifying pseudogout in large data sets is difficult due to its episodic nature and a lack of billing codes specific to this acute subtype of calcium pyrophosphate (CPP) deposition disease. The objective of this study was to evaluate a novel machine learning approach for classifying pseudogout using electronic health record (EHR) data.

Methods

We created an EHR data mart of patients with ≥1 relevant billing code or ≥2 natural language processing (NLP) mentions of pseudogout or chondrocalcinosis, 1991-2017. We selected 900 subjects for gold standard chart review for definite pseudogout (synovitis + synovial fluid CPP crystals), probable pseudogout (synovitis + chondrocalcinosis), or not pseudogout. We applied a topic modeling approach to identify definite/probable

SUBMITTER: Tedeschi SK 

PROVIDER: S-EPMC7338229 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature