构建年轻和中年妇科癌症患者心理痛苦的预测模型
Yitong Qu1, Yinan Zhang1, Xueying Zhou1
1Department of Nursing, Tianjin Medical University, Tianjin, China.
Journal of evaluation in clinical practice
|November 20, 2024
概括
人工神经网络 (ANN) 能够有效地预测年轻和中年妇科癌症患者的心理痛苦. 关键的风险因素包括自我效能,收入和睡眠时间,使早期识别和支持成为可能.
科学领域:
- 在瘤学瘤学.
- 人工智能在医学中的应用
- 心理健康 心理健康
背景情况:
- 妇科癌症患者经常经历严重的心理痛苦,影响生活质量.
- 预测这种痛苦是具有挑战性的,特别是在年轻和中年人群中.
- 早期识别风险患者对于及时干预至关重要.
研究的目的:
- 开发一种预测模型,用于年轻和中年妇科癌症患者的心理痛苦.
- 利用人工神经网络 (ANN) 来提高预测准确度.
- 将ANN性能与传统的物流回归 (LR) 进行比较.
主要方法:
- 一项涉及368名年轻和中年妇科癌症患者的横截面研究.
- 单变量分析以确定与心理痛苦相关的因素.
- 使用曲线下的面积 (AUC),灵敏度和特异性的ANN和LR模型的开发和比较.
主要成果:
- 自我效能,经济收入和睡眠时间被确定为心理痛苦的主要风险因素.
- 该ANN模型实现了0.977的优异AUC,具有94.83%的灵敏度和86.44%的特异性.
- LR模型显示AUC为0.920,敏感度为85.57%,特异性为82.76%.
结论:
- 在预测心理痛苦方面,ANN模型在LR模型上表现出显著的优势.
- 在这种患者群体中,ANN可以作为一种有价值的决策支持工具,用于早期检测心理困扰.
- 改进的预测有助于为妇科癌症患者提供及时和有针对性的支持性护理.
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