对于慢性病的基于深度学习的死亡预测模型
Hyeji Kim1,2, Hyekyung Woo1
1Department of Health Administration, Kongju National University, Gongju, Korea.
Healthcare informatics research
|November 20, 2025
概括
紧急诊所和长时间住院是慢性病患者死亡的关键预测因素. 人工智能驱动的模型可以帮助评估预后,指导及时干预,以获得更好的患者结果.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 慢性病 (CKD) 患病率在全球范围内不断增加,对死亡率作出重大贡献.
- 传统的统计方法正在被人工智能增强,用于预测CKD的死亡率因素.
- 进一步的人工智能研究对于了解慢性病患者死亡决定因素至关重要.
研究的目的:
- 通过机器学习,确定与CKD患者死亡率相关的关键因素.
- 开发一种基于深度学习的预测模型,用于评估CKD的死亡风险.
主要方法:
- 利用韩国疾病控制和预防局 (2016-2021) 的数据.
- 应用最小绝对收缩和选择运营商 (LASSO) 回归来确定显著的死亡预测因素.
- 开发了一个深度学习模型,包含LASSO选择的变量.
主要成果:
- 确定了八个影响死亡的因素,包括住院时间长度,急诊,年龄,严重程度调整得分和区域差异.
- 深度学习模型实现了高精度 (96.84%) 与低损失值 (0.1207).
结论:
- 紧急诊所和长时间住院是CKD死亡率的重要预测因素.
- 定期脏病监测和及时的脏替代疗法对于降低风险至关重要.
- 使用一般特征的基于AI的预测模型可以帮助快速评估预后.
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