使用机器学习来预测非计划的医院利用率和化疗管理,从患者报告的结果指标
Zuzanna Wójcik1, Vania Dimitrova2, Lorraine Warrington3
1UKRI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care, University of Leeds, Leeds, United Kingdom.
JCO clinical cancer informatics
|April 26, 2024
概括
患者报告的结果措施 (PROMs) 增强机器学习 (ML) 模型,用于预测住院和化疗变化. 平衡数据显著改善了模型性能,以获得更好的患者护理.
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:
- 化疗不良影响经常导致住院和治疗调整.
- 确定计划外医院利用率的预测因素对于提高医疗保健质量和患者福祉至关重要.
研究的目的:
- 评估患者报告的结果措施 (PROM) 是否改善机器学习 (ML) 模型的预测性能.
- 使用结合PROM的ML模型预测住院,分拣事件和化疗修改.
主要方法:
- 利用了接受化疗的508名参与者的临床试验数据.
- 在6个ML模型 (逻辑回归,决策树,自适应增强,随机森林,SVM,神经网络) 中比较了6个特征集 (临床数据,PROM和组合).
- 评估模型在预测医院入院,分组事件和化疗变化的性能,考虑到数据不平衡.
主要成果:
- PROMs显著提高了所有研究结果的预测准确性.
- 随机森林和SVM模型在平衡数据集中显示了预测入院和化疗变化的最高性能.
- 逻辑回归在不平衡的数据集上表现最好,数据平衡通常导致更好的预测性能.
结论:
- 应用于PROM数据的机器学习模型可以有效预测医院利用率和化疗管理.
- 这种方法有可能改善医疗保健规划,并使个性化癌症治疗成为可能.
- 该研究通过比较处理不平衡数据的方法来强调ML研究的最佳实践.
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