Ten quick tips for ensuring machine learning model validity

被引：0

作者：

Goh, Wilson Wen Bin ^{[1
,2
,3
,4
,5
]}

Kabir, Mohammad Neamul ^{[1
,3
]}

Yoo, Sehwan ^{[1
,3
]}

Wong, Limsoon ^{[6
,7
]}

机构：

[1] Nanyang Technol Univ, Lee Kong Chian Sch Med, Singapore, Singapore

[2] Nanyang Technol Univ, Sch Biol Sci, Singapore, Singapore

[3] Nanyang Technol Univ, Ctr Biomed Informat, Singapore, Singapore

[4] Nanyang Technol Univ, Ctr AI Med, Singapore, Singapore

[5] Imperial Coll London, Fac Med, Dept Brain Sci, Div Neurol, London, England

[6] Natl Univ Singapore, Sch Comp, Singapore, Singapore

[7] Natl Univ Singapore, Yong Loo Lin Sch Med, Singapore, Singapore

来源：

PLOS COMPUTATIONAL BIOLOGY | 2024年 / 20卷 / 09期

基金：

新加坡国家研究基金会;

关键词：

SEQUENCE;

D O I：

10.1371/journal.pcbi.1012402

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model validity is challenging. The 10 quick tips described here discuss useful practices on how to check AI/ML models from 2 perspectives-the user and the developer.

引用

页数：12

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