基于特征的深度神经网络方法用于预测COVID-19患者的死亡风险
Thing-Yuan Chang1, Cheng-Kui Huang2, Cheng-Hsiung Weng1,3
1Department of Information Management, National Chin-Yi University of Technology, Taichung 41130, Taiwan, Republic of China.
Engineering applications of artificial intelligence
|June 27, 2023
概括
这项研究介绍了一种混合深度神经网络 (DNN) 模型,用于预测COVID-19死亡风险. 增强型模型显著优于传统方法,以更少的特征实现高精度.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 医疗保健中的人工智能
背景情况:
- 预测COVID-19患者的死亡风险对于有效的临床管理至关重要.
- 现有的预测模型在识别高风险个体时可能缺乏准确性或效率.
研究的目的:
- 开发和评估一个针对COVID-19患者死亡风险的先进预测模型.
- 将深度神经网络 (DNN) 与特征选择和实例集群集成,以改善预测.
主要方法:
- 开发了一种混合方法,将深度神经网络 (DNN) 与特征选择和实例集群相结合.
- 该模型的性能使用12,020例COVID-19数据集和10种交叉验证方法进行了评估.
- 对比分析包括基于特征的DNN,基于集群的DNN,标准的DNN和多层感知器 (神经网络).
主要成果:
- 提出的基于特征的DNN模型表现出卓越的性能,实现了98.62%的回忆,91.99%的F1得分和91.41%的准确性.
- 仅使用前5个特征的DNN模型表现出与使用所有57个特征的模型相比的性能.
- 混合方法显著超过了原来的神经网络预测模型.
结论:
- 整合特征选择,实例集群和DNN显著提高COVID-19死亡风险预测.
- 开发的模型提供了高预测准确性与减少的功能集,提高效率.
- 这种方法为识别高风险COVID-19患者提供了强大的工具,有助于临床决策.
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