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临床预测融合网络,用于在患者队列中准确预测疾病

Md Zeyauddin¹, Shafiqul Abidin¹, Imran Khan²

¹Department of Computer Science , Aligarh Muslim University , Aligarh, India.

Scientific reports

|December 25, 2025

概括

临床预测融合网络 (CPFN) 通过自适应地融合多个机器学习模型,提供准确和可解释的医疗预测. 这种方法确保了跨不同患者数据集的可靠性能,以改善临床决策.

科学领域:

医疗保健中的人工智能
机器学习用于医疗数据分析
在临床预测中进行集体学习.

背景情况:

医疗数据的复杂性需要先进的预测模型.
现有的模型往往在不同数据集的准确性和可解释性方面扎.
需要适应性框架,能够处理多式联络患者信息.

研究的目的:

介绍临床预测融合网络 (CPFN),这是一个适应性集体学习框架.
评估CPFN在疾病特异性和融合多种疾病数据集上的表现.
展示CPFN对准确,可解释和可概括的临床预测的能力.

主要方法:

开发了CPFN,集成了后勤回归,随机森林和支持矢量机器分类器.
采用了基于验证的加权融合策略,用于自适应分类器加权.
利用心脏病学,神经病学,糖尿病,肺病学,瘤学和融合数据集的10倍分层交叉验证.

主要成果:

实现了高精度:在单个数据集上高达93.0±0.4%,在组合数据集上高达95.5±0.3%.
报告了强的绩效指标:回忆 (92.0±0.5%),F1得分 (92.5±0.4%) 和ROC-AUC (0.95-0.975).
在异质数据源中展示了一致和可概括的性能.

结论:

CPFN为医疗保健提供了一个可扩展的,数据驱动的决策支持框架.
该模型的透明设计增强了可复制性和临床适用性.
对于复杂的医疗保健数据挑战,CPFN代表了下一代预测系统.

关键词:

在医疗保健中的AI.临床决策系统临床决策系统组合学习学习组合学习预测性分析是一种预测性分析.治疗治疗预测治疗的预测.

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