在线机器学习模型用于预测慢性病老年患者的妄想风险:开发和初步验证
Lang Gao1, Guangdong Wang2, Xingyi Yang3
1Department of Critical Care Medicine, Clinical Medical College of Qinghai University, Xi Ning, 810000, China.
European journal of medical research
|November 27, 2025
概括
机器学习准确地预测老年慢性病 (CKD) 患者的妄想风险. 基于网络的计算器有助于在重症监护室 (ICU) 早期检测,改善患者的治疗结果.
科学领域:
- 老年医学 老年医学
- 腎臟病學 (nephrology) 是一種醫學.
- 密集护理医学 密集护理医学
- 医疗保健中的人工智能
背景情况:
- 痴呆症是慢性病 (CKD) 的老年患者的常见并发症,由多因素脆弱性加剧.
- 在老年重症监护室 (ICU) 进行传统的妄想评估,由于沟通缺陷而面临挑战.
- 机器学习提供了一种有前途的方法,通过复杂的模式识别来完善妄想预测.
研究的目的:
- 开发一个可解释的在线工具,以便及时识别老年CKD患者的高风险妄想症.
- 引导主动干预并提高ICU环境中的患者结果.
- 为了利用机器学习来改善妄想风险评估.
主要方法:
- 通过ICD代码识别的老年CKD患者;使用重症监护病房 (CAM-ICU) 混乱评估方法评估的妄想.
- 数据包括基线特征,生命体,实验室,并发症和临床分数;双特征选择使用拉索回归和Boruta.
- 使用决策曲线分析 (DCA) 和校准曲线评估了八种机器学习模型; 最好的模型被解释为SHAP值,并作为网络计算器部署.
主要成果:
- 通过双重特征选择,通过双重特征选择确定了十个错觉的关键预测因素.
- 梯度增强机 (GBM) 模型在内部和外部验证中显示出强大的校准和预测准确性.
- 在SHAP分析中,格拉斯哥昏迷度量 (GCS) 评分,SOFA评分和镇静剂使用作为重要预测因素;开发了一个基于网络的风险计算器.
结论:
- 开发的预测模型准确地评估了老年CKD患者的妄想风险,具有强大的性能和临床实用性.
- 基于Web的动态计算器提供了个性化的ICU风险评估.
- 该工具支持早期妄的识别和主动干预,以加强临床管理.
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