ArticleFrontiers in medical technology2024
Uncovering the burden of hidradenitis suppurativa misdiagnosis and underdiagnosis: a machine learning approach.
Article in Frontiers in medical technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- Reliability and Validity of the Serbian Version of the Hidradenitis Suppurativa Quality of Life Questionnaire (HSQoL-24).Journal of clinical medicine · 2026Article
- Defining Moderate Disease and Progression in Hidradenitis Suppurativa: An Expert Framework to Unlock the Window of Opportunity for Prompt Treatment.American journal of clinical dermatology · 2026Review
- Towards a Paradigm Shift in Delivering Hidradenitis Suppurativa Care: a Narrative Review.Dermatology and therapy · 2025Review
- Transcriptomic Signatures and Molecular Pathways in Hidradenitis Suppurativa-A Narrative Review.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.