预测癌症症状轨迹使用纵向电子健康记录数据和长期短期记忆神经网络
Sena Chae1, W Nick Street2, Naveenkumar Ramaraju3
1The University of Iowa College of Nursing, Iowa City, IA.
JCO clinical cancer informatics
|March 12, 2024
概括
预测癌症症状轨迹是可以使用过去的经验. 机器学习模型,包括LSTM,在更好的患者护理和生活质量方面优于传统方法.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 准确预测癌症症状严重程度和进展对于及时的临床干预和治疗计划至关重要.
- 目前的预测方法通常受到稀疏,不一致的数据和简单的措施 (如最后观察到的症状严重程度) 的限制.
- 开发强大的预测模型对于改善癌症护理中的患者结果和生活质量至关重要.
研究的目的:
- 基于历史症状数据,开发和评估未来癌症症状体验的预测模型.
- 为了比较不同机器学习模型在预测症状轨迹方面的表现.
- 为了提高癌症症状预测,利用例行收集的护理文档.
主要方法:
- 对208名住院癌症患者的病历 (2008-2014) 进行了回顾性纵向分析.
- 长期短期记忆 (LSTM) 循环神经网络,线性回归和随机森林模型的培训和评估.
- 在过去的症状数据上训练模型,以预测未来的症状轨迹.
主要成果:
- 至少有一种测试模型 (LSTM,线性回归,随机森林) 超过了仅基于先前临床观察的预测.
- 在预测恶心和心理社会状态方面,LSTM模型显著优于线性回归和随机森林.
- 线性回归在预测口腔健康方面表现出色,而随机森林在移动性和营养预测方面优越.
结论:
- 经常收集的护理文档,即使数据稀少,也可以用来成功预测患者的症状轨迹.
- 开发的预测模型可以帮助个性化癌症患者的症状管理策略.
- 改善症状预测有潜力显著支持癌症患者的生活质量.
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