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确保机器学习模型有效性的十个快速技巧。

Ten quick tips for ensuring machine learning model validity.

机构信息

Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.

出版信息

PLoS Comput Biol. 2024 Sep 19;20(9):e1012402. doi: 10.1371/journal.pcbi.1012402. eCollection 2024 Sep.

DOI:10.1371/journal.pcbi.1012402
PMID:39298376
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11412509/
Abstract

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.

摘要

人工智能（AI）和机器学习（ML）模型越来越多地被部署在生物医学和健康数据上，以深入了解生物学机制、预测疾病结果，并支持临床决策。然而，确保模型的有效性具有挑战性。这里描述的 10 个快速提示从用户和开发人员两个角度讨论了检查 AI/ML 模型的有用实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/834d550bc1c5/pcbi.1012402.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/f2c0c9ce1b1a/pcbi.1012402.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/9395e2367139/pcbi.1012402.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/6cf2707de7c6/pcbi.1012402.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/834d550bc1c5/pcbi.1012402.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/f2c0c9ce1b1a/pcbi.1012402.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/9395e2367139/pcbi.1012402.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/6cf2707de7c6/pcbi.1012402.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6d7/11412509/834d550bc1c5/pcbi.1012402.g004.jpg

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本文引用的文献

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Genome sequence and annotation of Periconia digitata a hopeful biocontrol agent of phytopathogenic oomycetes.
地钱内生真菌 Periconia digitata 的基因组序列和注释：一种有希望的植物病原卵菌生防菌。
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