The DERMACLEAR study: Verification results of a natural language processing system in dermatology
Abstract Background Accurately determining the epidemiology of dermatological diseases such as hidradenitis suppurativa (HS), psoriasis (PsO), chronic urticaria (CU) and/or atopic dermatitis (AD) is challenging due to variations in prevalence and disease severity in the reported literature. Objectiv...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-12-01
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| Series: | JEADV Clinical Practice |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/jvc2.217 |
| _version_ | 1797435246905393152 |
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| author | Francisco J. Ortiz de Frutos Ana M. Giménez‐Arnau Lluís Puig Juan F. Silvestre Esther Serra Laura Salgado‐Boquete Vicente García‐Patos Jose L. L. Estebaranz Jaime Notario Ana Martin‐Santiago Gabriel M. Pontevia Víctor Martín Guillermo Guinea Pau Terradas Esteban Daudén |
| author_facet | Francisco J. Ortiz de Frutos Ana M. Giménez‐Arnau Lluís Puig Juan F. Silvestre Esther Serra Laura Salgado‐Boquete Vicente García‐Patos Jose L. L. Estebaranz Jaime Notario Ana Martin‐Santiago Gabriel M. Pontevia Víctor Martín Guillermo Guinea Pau Terradas Esteban Daudén |
| author_sort | Francisco J. Ortiz de Frutos |
| collection | DOAJ |
| description | Abstract Background Accurately determining the epidemiology of dermatological diseases such as hidradenitis suppurativa (HS), psoriasis (PsO), chronic urticaria (CU) and/or atopic dermatitis (AD) is challenging due to variations in prevalence and disease severity in the reported literature. Objectives The DERMACLEAR study aims to use natural language processing (NLP) to assess the proportions of patients with HS, PsO, CU and/or AD, and obtain information on patient profiles, patient journeys, and disease and healthcare burden in Spain. Here, the study design and objectives of the DERMACLEAR study are described and the precision of the NLP system used is assessed. Methods This study will retrospectively collect patient information from electronic health records (EHRs) at dermatology departments from seven tertiary hospitals in Spain. The NLP system was developed by IOMED Medical Solutions and was verified internally (IOMED scientific team) and externally (principal investigators of each hospital) to determine its precision in identifying patients with HS, PsO, CU and/or AD. Furthermore, internal verification was performed on other medical variables relevant to the study. Results To date, the DERMACLEAR study has retrospectively collected data from 54,458 patients with HS, PsO, CU and/or AD (HS: 5045; PsO: 32,559; CU: 8397; AD: 12,492). The average precision of the NLP system to identify patients diagnosed with HS, PsO, CU, and/or AD across all hospitals exceeded 95% via external and internal verification. Conclusions Results from the DERMACLEAR study will increase the real‐world evidence of clinical practice, obtaining a large amount of information on patients with the studied diseases. The NLP system used is precise in identifying patients diagnosed with HS, PsO, CU and/or AD, and other medical variables from EHRs, highlighting that it is a valid system to use in the DERMACLEAR study. |
| first_indexed | 2024-03-09T10:45:25Z |
| format | Article |
| id | doaj.art-5fd09b7f16c4403ba7b16514dc4709a6 |
| institution | Directory Open Access Journal |
| issn | 2768-6566 |
| language | English |
| last_indexed | 2024-03-09T10:45:25Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | JEADV Clinical Practice |
| spelling | doaj.art-5fd09b7f16c4403ba7b16514dc4709a62023-12-01T10:43:26ZengWileyJEADV Clinical Practice2768-65662023-12-012477578510.1002/jvc2.217The DERMACLEAR study: Verification results of a natural language processing system in dermatologyFrancisco J. Ortiz de Frutos0Ana M. Giménez‐Arnau1Lluís Puig2Juan F. Silvestre3Esther Serra4Laura Salgado‐Boquete5Vicente García‐Patos6Jose L. L. Estebaranz7Jaime Notario8Ana Martin‐Santiago9Gabriel M. Pontevia10Víctor Martín11Guillermo Guinea12Pau Terradas13Esteban Daudén14Hospital Universitario 12 de Octubre Universidad Complutense Madrid SpainDepartment of Dermatology, Hospital del Mar‐IMIM Universitat Pompeu Fabra Barcelona SpainServicio de Dermatología, IIB SANT PAU Hospital de la Santa Creu i Sant Pau Barcelona SpainHospital General Universitario Dr Balmis Alicante SpainServicio de Dermatología, IIB SANT PAU Hospital de la Santa Creu i Sant Pau Barcelona SpainComplejo Hospitalario Universitario de Pontevedra Pontevedra SpainHospital Universitari Vall d'Hebron Barcelona SpainHospital Universitario Fundación Alcorcón Madrid SpainHospital de Bellvitge Barcelona SpainHospital Universitari Son Espases Palma de Mallorca SpainIOMED Medical Solutions Barcelona SpainNovartis Farmacéutica S.A Barcelona SpainNovartis Farmacéutica S.A Barcelona SpainNovartis Farmacéutica S.A Barcelona SpainDepartment of Dermatology, Hospital Universitario de la Princesa Instituto de Investigación Sanitaria (IIS‐HP) Madrid SpainAbstract Background Accurately determining the epidemiology of dermatological diseases such as hidradenitis suppurativa (HS), psoriasis (PsO), chronic urticaria (CU) and/or atopic dermatitis (AD) is challenging due to variations in prevalence and disease severity in the reported literature. Objectives The DERMACLEAR study aims to use natural language processing (NLP) to assess the proportions of patients with HS, PsO, CU and/or AD, and obtain information on patient profiles, patient journeys, and disease and healthcare burden in Spain. Here, the study design and objectives of the DERMACLEAR study are described and the precision of the NLP system used is assessed. Methods This study will retrospectively collect patient information from electronic health records (EHRs) at dermatology departments from seven tertiary hospitals in Spain. The NLP system was developed by IOMED Medical Solutions and was verified internally (IOMED scientific team) and externally (principal investigators of each hospital) to determine its precision in identifying patients with HS, PsO, CU and/or AD. Furthermore, internal verification was performed on other medical variables relevant to the study. Results To date, the DERMACLEAR study has retrospectively collected data from 54,458 patients with HS, PsO, CU and/or AD (HS: 5045; PsO: 32,559; CU: 8397; AD: 12,492). The average precision of the NLP system to identify patients diagnosed with HS, PsO, CU, and/or AD across all hospitals exceeded 95% via external and internal verification. Conclusions Results from the DERMACLEAR study will increase the real‐world evidence of clinical practice, obtaining a large amount of information on patients with the studied diseases. The NLP system used is precise in identifying patients diagnosed with HS, PsO, CU and/or AD, and other medical variables from EHRs, highlighting that it is a valid system to use in the DERMACLEAR study.https://doi.org/10.1002/jvc2.217deep learningdermatologymachine learningnatural language processingOMOP CSMreal world data |
| spellingShingle | Francisco J. Ortiz de Frutos Ana M. Giménez‐Arnau Lluís Puig Juan F. Silvestre Esther Serra Laura Salgado‐Boquete Vicente García‐Patos Jose L. L. Estebaranz Jaime Notario Ana Martin‐Santiago Gabriel M. Pontevia Víctor Martín Guillermo Guinea Pau Terradas Esteban Daudén The DERMACLEAR study: Verification results of a natural language processing system in dermatology JEADV Clinical Practice deep learning dermatology machine learning natural language processing OMOP CSM real world data |
| title | The DERMACLEAR study: Verification results of a natural language processing system in dermatology |
| title_full | The DERMACLEAR study: Verification results of a natural language processing system in dermatology |
| title_fullStr | The DERMACLEAR study: Verification results of a natural language processing system in dermatology |
| title_full_unstemmed | The DERMACLEAR study: Verification results of a natural language processing system in dermatology |
| title_short | The DERMACLEAR study: Verification results of a natural language processing system in dermatology |
| title_sort | dermaclear study verification results of a natural language processing system in dermatology |
| topic | deep learning dermatology machine learning natural language processing OMOP CSM real world data |
| url | https://doi.org/10.1002/jvc2.217 |
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