关于癌症诊断,治疗和预后的机器学习研究的方法和报告质量

Aref Smiley1, David Villarreal-Zegarra1, C Mahony Reategui-Rivera1

  • 1Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.

Frontiers in oncology
|April 29, 2025
PubMed
概括

这项研究发现机器学习 (ML) 瘤学研究中存在重大报告缺陷,特别是在数据处理和模型透明度方面,尽管偏差风险很低. 提高报告质量对于可靠的ML癌症诊断和预后至关重要.

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