Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study
Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic dermatitis can only be identified if they have a hospital-diagnosed atopic dermatitis. The purpose of this study was to develop a machine learning model...
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Medical Journals Sweden
2025-05-01
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| Series: | Acta Dermato-Venereologica |
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| Online Access: | https://medicaljournalssweden.se/actadv/article/view/42250 |
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| author | Mie Sylow Liljendahl Kristina Ibler Christian Vestergaard Lone Skov Pavika Jain Jan Håkon Rudolfsen Ann Hærskjold Mathias Torpet |
| author_facet | Mie Sylow Liljendahl Kristina Ibler Christian Vestergaard Lone Skov Pavika Jain Jan Håkon Rudolfsen Ann Hærskjold Mathias Torpet |
| author_sort | Mie Sylow Liljendahl |
| collection | DOAJ |
| description | Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic dermatitis can only be identified if they have a hospital-diagnosed atopic dermatitis. The purpose of this study was to develop a machine learning model to identify all patients with atopic dermatitis by proxy, using data for contacts with primary care, prescription medication, and hospital contacts not related to skin diseases. Individuals redeeming a prescription for dermatological preparations were extracted as potential patients with atopic dermatitis. Individuals with a registered hospital diagnosis of atopic dermatitis were classified as “Known AD”, “Other skin disease” (registrations of other dermatological diagnosis codes indicating other skin disease), or “Uncertain AD status”’ (no hospital diagnosis registered). Patients categorized as “Known AD” and “Other skin disease” were used to develop the model. All uses of healthcare services 2 years prior to hospital diagnosis were used as potential predictors. The data were split into training and validation sets (70/30). From 1996 to 2022, 385,135 individuals had uncertain atopic dermatitis status. The most important predictors were corticosteroid prescriptions for dermatological use, consultations with dermatologist, and age. Of the 385,135 individuals, the model predicted that 230,522 individuals likely have atopic dermatitis.
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| format | Article |
| id | doaj-art-b0fe9ae122934d79b7d11e8cb2916916 |
| institution | DOAJ |
| issn | 0001-5555 1651-2057 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Medical Journals Sweden |
| record_format | Article |
| series | Acta Dermato-Venereologica |
| spelling | doaj-art-b0fe9ae122934d79b7d11e8cb29169162025-08-20T02:58:18ZengMedical Journals SwedenActa Dermato-Venereologica0001-55551651-20572025-05-0110510.2340/actadv.v105.42250Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register StudyMie Sylow Liljendahl0Kristina Ibler1Christian Vestergaard2Lone Skov3Pavika Jain4Jan Håkon Rudolfsen5Ann Hærskjold6Mathias Torpet7Department of Dermatology and Allergy, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, DenmarkDepartment of Dermatology, Bispebjerg Hospital, Copenhagen University Hospital, DenmarkDepartment of Dermatology, Aarhus University Hospital, Aarhus, DenmarkDepartment of Dermatology and Allergy, Copenhagen University Hospital - Herlev and Gentofte, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, DenmarkMarket Access, Sanofi Denmark A/SEY, Frederiksberg, DenmarkDepartment of Dermatology, Bispebjerg Hospital, Copenhagen University Hospital, DenmarkMedical Affairs, Sanofi Denmark A/SAtopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic dermatitis can only be identified if they have a hospital-diagnosed atopic dermatitis. The purpose of this study was to develop a machine learning model to identify all patients with atopic dermatitis by proxy, using data for contacts with primary care, prescription medication, and hospital contacts not related to skin diseases. Individuals redeeming a prescription for dermatological preparations were extracted as potential patients with atopic dermatitis. Individuals with a registered hospital diagnosis of atopic dermatitis were classified as “Known AD”, “Other skin disease” (registrations of other dermatological diagnosis codes indicating other skin disease), or “Uncertain AD status”’ (no hospital diagnosis registered). Patients categorized as “Known AD” and “Other skin disease” were used to develop the model. All uses of healthcare services 2 years prior to hospital diagnosis were used as potential predictors. The data were split into training and validation sets (70/30). From 1996 to 2022, 385,135 individuals had uncertain atopic dermatitis status. The most important predictors were corticosteroid prescriptions for dermatological use, consultations with dermatologist, and age. Of the 385,135 individuals, the model predicted that 230,522 individuals likely have atopic dermatitis. https://medicaljournalssweden.se/actadv/article/view/42250atopic dermatitismachine learninghealthcare registerprediction |
| spellingShingle | Mie Sylow Liljendahl Kristina Ibler Christian Vestergaard Lone Skov Pavika Jain Jan Håkon Rudolfsen Ann Hærskjold Mathias Torpet Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study Acta Dermato-Venereologica atopic dermatitis machine learning healthcare register prediction |
| title | Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study |
| title_full | Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study |
| title_fullStr | Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study |
| title_full_unstemmed | Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study |
| title_short | Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study |
| title_sort | identifying mild to moderate atopic dermatitis using a generic machine learning approach a danish national health register study |
| topic | atopic dermatitis machine learning healthcare register prediction |
| url | https://medicaljournalssweden.se/actadv/article/view/42250 |
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