使用机器学习在癌症息护理中预测不可见症状的新方法
Kazuki Shimada1, Satoru Tsuneto2
1Department of Palliative Medicine, Kyoto University Hospital, 54 Kawaharacho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan. mobile_pcu@kuhp.kyoto-u.ac.jp.
Scientific reports
|July 26, 2023
概括
机器学习可以准确地预测癌症患者无法口头沟通的非可见症状. 这种决策树分析有助于更好地评估和管理息治疗患者的症状.
科学领域:
- 在瘤学瘤学.
- 医疗保健中的机器学习
- 抚慰性护理是一种缓解性护理.
背景情况:
- 生命末期的癌症患者往往会失去口头沟通能力.
- 准确的症状评估对于有效的息护理至关重要.
- 机器学习为医疗保健应用提供了先进的预测能力.
研究的目的:
- 评估非语言沟通的癌症患者的非可见症状.
- 开发一种用于预测这些症状的机器学习模型.
- 在这种情况下,评估决策树分析的有效性.
主要方法:
- 213名癌症患者的回顾性临床调查 (2015年8月 - 2016年8月).
- 将症状分为可见和不可见的类别.
- 应用决策树分析 (一种机器学习方法) 来利用背景数据和可见症状预测非可见症状.
主要成果:
- 该模型预测了诸如疼痛,呼吸障碍,疲劳和焦虑等非可见症状.
- 在某些预测中,预测准确度达到88.0%,灵敏度为84.9%,特异性为96.7%.
- 证明了机器学习在识别与医疗保健专业人员相似的症状方面的潜力.
结论:
- 决策树分析可以有效地预测息性癌症护理中的非可见症状.
- 这种方法有助于改善非语言患者的症状评估和管理.
- 突出了机器学习在理解复杂患者需求方面的创新应用.
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