监督机器学习应用在护理笔记中,以确定儿童癌症患者需要心理支持
Akseli Reunamo1, Hans Moen2, Sanna Salanterä3,4
1Department of Computing, University of Turku, Turku, Finland.
机器学习可以从护理笔记中识别需要心理支持的儿童癌症患者. 这种方法有助于早期发现幸存者的心理健康需求,改善长期护理.
科学领域:
- 计算瘤学
- 数字健康
- 医疗保健中的机器学习
背景情况:
- 儿童癌症幸存者面临更大的心理健康风险和适应性挑战.
- 积极评估心理社会支持需求对于幸存者的福祉至关重要.
- 识别有风险的个体需要有效和准确的查方法.
研究的目的:
- 对文本分类进行监督机器学习的评估.
- 识别儿童癌症患者需要从护理笔记中获得心理社会支持.
- 专注于诊断后至少一年的患者.
主要方法:
- 使用三种机器学习模型进行文本分类.
- 在1672名患者的自由文字护理笔记上训练和评估模型.
- 使用嵌套交叉验证策略进行可靠的评估.
主要成果:
- 在癌症和糖尿病患者身上训练的随机森林模型显示出卓越的性能.
- 癌症患者的接受器操作特征曲线下的平均面积为0.798.
- 与仅对癌症患者进行训练的神经网络模型相比,在统计上有显著的改善.
结论:
- 机器学习有效地预测了儿童癌症患者的心理支持需求.
- 护士笔记可以成为基于机器学习的心理社会风险评估的有价值的数据来源.
- 这项技术可以在诊断后很长时间帮助识别需要心理健康支持的患者.
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