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 models. The results of the models developed in this study could be input into the development of an impact of inaction model that determines the cost implications of HS diagnosis and treatment delay to the healthcare system.

Keywords: dermatology; diagnosis; hidradenitis suppurativa; machine learning; model; prediction.

Figure 1

Modeling approach: (A) main modeling steps, (B) model development overview, (C) selection of hidradenitis suppurativa (HS) and control patients for model development.

Figure 2

Heat map of estimated hidradenitis suppurativa (HS) underdiagnosis among abscess and cellulitis patients by Metropolitan Statistical Areas (MSA) regions based on the prediction of the three top-performing machine learning (ML) models: (A) potential HS cases among abscess patients (AdaBoost), (B) potential HS cases among abscess patients (LightGBM), (C) potential HS cases among abscess patients (MaxVoting), (D) potential HS cases among cellulitis patients (AdaBoost), (E) potential HS cases among cellulitis patients (LightGBM), and (F) potential HS cases among cellulitis patients (MaxVoting).