Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach.

ABSTRACT: Hidradenitis suppurativa (HS) is a chronic inflammatory follicular skin condition that is associated with significant psychosocial and economic burden and a diminished quality of life and work productivity. Accurate diagnosis of HS is challenging due to its unknown etiology, which can lead to underdiagnosis or misdiagnosis that results in increased patient and healthcare system burden. We applied machine learning (ML) to a medical and pharmacy claims database using data from 2000 through 2018 to develop a novel model to better understand HS underdiagnosis on a healthcare system level. The primary results demonstrated that high-performing models for predicting HS diagnosis can be constructed using claims data, with an area under the curve (AUC) of 81%-82% observed among the top-performing m

SUBMITTER: Kirby J 

PROVIDER: S-EPMC10999681 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature