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Balancing Clinical Applicability and Scientific Depth in ML Models for MDA5-DM Prognosis: Response to 'From machine learning to clinical practice: phenotypic clusters of anti-MDA5 antibody-positive dermatomyositis'. By Koopman, Jacob; Buhler, Katherine; Choi, May
- Source :
- Briefings in Bioinformatics; Jul2024, Vol. 25 Issue 4, p1-2, 2p
- Publication Year :
- 2024
-
Abstract
- This article discusses the potential of machine learning (ML) models to improve the phenotypic stratification of idiopathic inflammatory myopathies (IIMs), with a focus on anti-MDA5-positive dermatomyositis (MDA5-DM). MDA5-DM is a subgroup of dermatomyositis that is commonly associated with comorbidities, particularly rapidly progressing interstitial lung disease (RP-ILD). However, the variability in patient cohort sizes, clinical characteristics, and lack of standardized diagnostic criteria for MDA5-DM pose challenges for ML models. The article emphasizes the need for unbiased and consistent data across studies to achieve rigorous replication of ML models and highlights the importance of integrating scientific insights with the development of practical and easily applicable clinical variables. [Extracted from the article]
- Subjects :
- DERMATOMYOSITIS
MACHINE learning
PROGNOSIS
AUTOANTIBODIES
Subjects
Details
- Language :
- English
- ISSN :
- 14675463
- Volume :
- 25
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Briefings in Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 178650371
- Full Text :
- https://doi.org/10.1093/bib/bbae295