Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods.

ABSTRACT:

Background

There are no exact data about the prevalence of familial chylomicronemia syndrome (FCS) in Central Europe. We aimed to identify FCS patients using either the FCS score proposed by Moulin et al. or with data mining, and assessed the diagnostic applicability of the FCS score.

Methods

Analyzing medical records of 1,342,124 patients, the FCS score of each patient was calculated. Based on the data of previously diagnosed FCS patients, we trained machine learning models to identify other features that may improve FCS score calculation.

Results

We identified 26 patients with an FCS score of ≥10. From the trained models, boosting tree models and support vector machines performed the best for patient recognition with overall AUC above 0.95, while artificial neural n

SUBMITTER: Nemeth A 

PROVIDER: S-EPMC9331828 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature