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深度学习方法用于预测癌症患者在化疗期间的身体和精神恶化

Joseph Finkelstein¹, Aref Smiley¹, Christina Echeverria²

¹Department of Biomedical Informatics, The University of Utah, Salt Lake City, UT 84108, USA.

Diagnostics (Basel, Switzerland)

|May 1, 2025

概括

深度学习模型准确地预测化疗期间的身体和精神症状升级,使积极的患者护理成为可能. 将症状按3天间隔分组改善了更好的干预措施的预测准确性.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

预测化疗患者的症状升级对于及时干预和改善结果至关重要.
在化疗期间自我报告的身体和精神症状需要准确的预测.

研究的目的:

采用深度学习模型来预测化疗期间12种自我报告的症状的恶化.
为量身定制的预测模型将症状分为身体和精神组.

主要方法:

利用化疗患者每日自我报告的症状日志.
应用卷积神经网络 (CNN),长期短期记忆 (LSTM) 和门式循环单元 (GRU) 模型.
将症状分组为3至7天的间隔,以解决阶级失衡问题,训练80%的数据,评估20%的数据.

主要成果:

三天间隔显示出最佳的预测性能.
对于身体症状,CNN的准确率达到79.2%;对于精神症状,GRU的准确率达到77.2%.
模型性能随着更长的间隔而下降,尽管CNN和GRU显示相对稳定.

结论:

分类症状可以提高化学疗法期间对身体和精神健康的预测准确度.
深度学习模型显示出预测瘤护理中的症状升级的巨大潜力.
将预测模型集成到临床工作流程中可以促进主动的症状管理,并改善患者的护理.

关键词:

在这里,我们可以看到AIAIAI.人工智能驱动的症状监测监测.在美国,CNN是CNN.格鲁格鲁模型的模型这是LSTM的LSTM.化学疗法症状预测和预测数字健康数字健康瘤学症状管理症状管理

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