Machine Learning for Early Detection of Hidradenitis Suppurativa: A Feasibility Study Using Medical Insurance Claims Data
Open accessAbstract
Patients with hidradenitis suppurativa (HS) are often misdiagnosed and may wait up to 10 years to receive a diagnosis of HS. This study aimed to predict HS diagnosis prior to actual diagnosis on the basis of previous medical history using models developed with insurance claims data. Three machine learning models were compared with a model using features selected by a dermatologist (clinical baseline model). The study analyzed 5,900,000 United States individuals' insurance records over 13.5 years. The population included 13,886 patients with HS with at least 1 claim in each of the 2 years prior to their first HS diagnosis and 69,428 control patients with no HS diagnosis. The models aimed to classify HS diagnosis status on the basis of clinical features observed over 2 years. Model performance was assessed by area under the receiver operating characterisitic curve, F1-score, and precision and recall rates. The machine learning models (logistic regression, random forest, and XGBoost) showed a higher area under the receiver operating characterisitic curve than the clinical baseline model (logistic regression = 0.75, random forest = 0.79, XGBoost = 0.80, clinical = 0.71). In the clinical model and the best-performing XGBoost model, the top features associated with diagnosis were patient age at prediction and sex. The XGBoost model top features also included the use of sulfamethoxazole/trimethoprim and clindamycin phosphate and obesity.
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An XGBoost model identified hidradenitis suppurativa from claims data, with top features including sulfamethoxazole/trimethoprim use, clindamycin phosphate use, and obesity diagnosis.
“From claims data, Ali et al (2025) identified HS with XGBoost model and identified top features that include the use of sulfamethoxazole/trimethoprim, use of clindamycin phosphate, and having an obesity diagnosis.”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Ali et al, 2025
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Cites
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Artificial intelligence–enabled precision medicine for inflammatory skin diseases
Tang, A. S., Wei, M. L., Haemel, A., La, C., Sirota, M., & Lee, E. Y. (2026) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.10.596 · Cited by 6 · see citing works
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Artificial intelligence-enabled precision medicine for inflammatory skin diseasesPreprint
Tang, A., Wei, M. L., Haemel, A. K., La, C., Sirota, M., & Lee, E. Y. (2025) · PubMed · doi:10.48550/arxiv.2505.09527 · Cited by 5 · see citing works
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Clinically relevant evidence on imaging and artificial intelligence in hidradenitis suppurativa
Wortsman, X., Boostani, M., Kiss, N., Dini, V., & Ioannides, D. (2026) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.70716
- Authors
- Ali, W., Williams, J., Xiong, B., Zou, J., & Daneshjou, R.
- Affiliations
- UCB Pharma (United Kingdom) (GB)
- UCB Pharma (Belgium) (BE)
- Stanford University (US)
- Corresponding author
- Ali, Waqar
- Venue
- JID Innovations
- Publisher
- Elsevier BV
- Published
- 2025-05
- Volume
- 5
- Issue
- 3
- Pages
- 100362
- ISSN
- 2667-0267 (ISSN-L)
- PMC
- PMC11986968
- OpenAlex
- W4408300685
- Cited by
- 4
- References
- 15
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- JID Innovations (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- UCB Pharma (GPP2022)
- UCB SA
- Topics
- Health Sciences › Medicine › Dermatology › Hidradenitis Suppurativa and Treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- hidradenitis suppurativa, early diagnosis, health insurance claims, machine learning prediction, XGBoost, random forest, logistic regression, misdiagnosis, clinical features, trimethoprim-sulfamethoxazole, clindamycin, obesity, patient age and sex
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