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整合多任务和成本敏感的学习,以预测老年人慢性疾病的死亡风险,使用现实世界的数据

Aosheng Cheng¹, Yan Zhang², Zhiqiang Qian³

¹Center for Studies of Information Resources, Wuhan University, Wuhan, China.

International journal of medical informatics

|July 28, 2024

概括

这项研究开发了一个深度学习框架,从现实数据中预测老年人死亡风险,有效处理多种慢性疾病和阶级不平衡,以提高准确性.

科学领域:

计算生物学和生物信息学
机器学习在医疗保健中的应用
流行病学和公共卫生.

背景情况:

现实世界的数据为老年人的慢性疾病死亡率提供了多样化的见解.
现有的深度学习模型在死亡率预测中往往忽略了并发症和阶级不平衡.
在患有多种慢性疾病的老年患者中,准确预测死亡风险是具有挑战性的.

研究的目的:

开发一个深度学习框架,使用现实数据准确预测死亡风险.
为了应对伴随性疾病和老年人死亡率预测中的极端阶级不平衡的挑战.
为了提高预测准确性,利用多任务和成本敏感的学习.

主要方法:

整合多任务学习和成本敏感学习到深度神经网络架构中.
开发了一个模型来预测跨多种慢性疾病的死亡风险,共享疾病间复杂性的参数.
集成的成本敏感功能,以改善学习的积极的类特征和处理类不平衡.

主要成果:

该研究分析了482,145名患有15种慢性疾病的患者的数据,其中包括9516例死亡.
拟议的多任务学习成本敏感深度神经网络 (MTL-CSDNN) 在预测准确度方面表现优于其他六种模型.
MTL-CSDNN实现了高性能指标,包括ACC=0.99,REC=0.99,PRAUC=0.97,MCC=0.98,以及G-平均值=0.98.

结论:

开发的方法提供了一个精确的方法,用于使用现实世界的数据预测多种疾病的死亡风险.
这一框架可以优化慢性疾病管理,改善老年人的福祉.
潜在的应用包括降低医疗保健成本和增强老年护理策略.

关键词:

老年人是人口中的老年人.这是一个MTL-CSDNN模型.预测死亡率风险的预测.多种慢性疾病多种慢性疾病.现实世界的数据数据.

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