Using machine learning to improve anaphylaxis case identification in medical claims data.

ABSTRACT:

Objectives

Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.

Materials and methods

This study used claims data obtained between October 1, 2015 and February 28, 2019 from the CMS database to examine the utility of machine learning in identifying incident anaphylaxis cases. We created a feature selection pipeline to identify critical features between different datasets. Then a variety of unsupervised and supervised methods were used (eg, Sammon mapping and eXtreme Gradient Boosting) to train models on datasets of differing data quality, which reflects the varying availability and potential rarity of ground truth data in medical da

SUBMITTER: Kural KC 

PROVIDER: S-EPMC11190610 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature