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机器学习算法用于预测乳腺癌复发,使用来自电子健康记录的结构化和非结构化来源

Lorena González-Castro¹, Marcela Chávez², Patrick Duflot²

¹School of Telecommunication Engineering, University of Vigo, 36310 Vigo, Spain.

|June 22, 2023

概括

机器学习模型可以预测乳腺癌5年复发. 结构化临床数据,当与XGBoost一起使用时,提供了最好的预测准确性,优于组合数据源的性能.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

乳腺癌 (BC) 复发显著影响患者的死亡率.
机器学习 (ML) 提供了利用医疗保健数据改善患者风险分层的潜力.
研究将结构化和非结构化数据源结合起来,以提高BC复发预测.

研究的目的:

评估ML算法在预测乳腺癌5年复发的有效性.
使用结构化数据,非结构化 (自由文本) 数据和两者的组合来比较预测性能.
确定乳腺癌复发风险分层的最佳数据源和ML模型.

主要方法:

收集并预处理了823名乳腺癌患者的临床数据.
从结构化的临床信息和非结构化的自由文本临床笔记中衍生出来的特征.
评估了五个ML算法,包括XGBoost,用于预测5年复发.
使用精度,回忆,F1得分和AUROC评估模型性能.

主要成果:

该XGBoost模型显示了最高的性能 (AUROC = 0.807).
结构化数据产生了最好的预测结果,其次是非结构化数据.
综合数据集 (结构化和非结构化) 的表现最差.

结论:

ML算法对于乳腺癌复发风险分层和患者监测有价值.
结构化临床数据为基于ML的复发预测提供了卓越的性能.
自然语言处理方法提供与结构化数据相比的结果,可能需要更少的预处理工作.

关键词:

乳腺癌乳腺癌乳腺癌机器学习是机器学习.患者分层是患者的分层.复发预测的复发预测二次使用的二次用途.结构化数据是结构化数据.非结构化数据是非结构化数据.

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