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Machine learning classification meets migraine: recommendations for study evaluation
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DOI：10.1186/s10194-024-01924-x.png)
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摘要
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The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classification of migraine types and subtypes. However, inconsistencies in study design, lack of methodological transparency, and the absence of external validation limit the impact and reproducibility of such studies. This paper presents a framework of six essential recommendations for evaluating ML-based classification in migraine research: (1) group homogenization by clinical phenotype, attack frequency, comorbidity, therapy, and demographics; (2) defining adequate sample size; (3) quality control of collected and preprocessed data; (4) transparent training, testing, and performance evaluation of ML models, including strategies for data splitting, overfitting control, and feature selection; (5) interpretability of results with clinical relevance; and (6) open data and code sharing to facilitate reproducibility. These recommendations aim to balance the trade-off between model generalization and precision while encouraging collaborative standardization across the ML and headache communities. Furthermore, this framework intends to stimulate discussion toward forming a consortium to establish definitive guidelines for ML-based classification research in migraine field.
Keyword:
Benchmark
Machine learning classification models
Data quality
Model interpretability
Model reproducibility
Migraine types
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7.9
论文数:
3.1K
被引数:
9.0K
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引用论文
Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition国际头痛学会 (IHS) 头痛分类委员会国际头痛疾病分类，第3版.png)
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