在慢性阻塞性肺病的临床建模中使用分布式机器学习的实践
Junfeng Peng1, Xujiang Liu1, Ziwei Cai1
1Department of Computer Science and Engineering, Guangdong University of Education, Guangzhou 510303, China.
Heliyon
|July 29, 2024
概括
这项研究引入了COPD平均联合学习 (COPD_AVG_FL),以整合分散的患者数据,以便更好地诊断慢性阻塞性肺病 (COPD). 该系统显著提高了诊断准确度,同时保护了患者的隐私.
科学领域:
- 数字健康数字健康
- 医疗信息学医学信息学
- 机器学习在医疗保健中的应用
背景情况:
- 慢性阻塞性肺病 (COPD) 由于高患病率,发病率和分散在医疗单位的异质数据而带来挑战.
- 整合这些分散的数据是昂贵的,并且由于患者隐私法规而复杂,导致数据孤岛.
- 这些数据岛阻碍了综合分析和COPD先进数字健康解决方案的开发.
研究的目的:
- 开发用于诊断慢性阻塞性肺病 (COPD) 的分布式系统,克服数据碎片化和隐私问题.
- 通过使用联合学习原则实现高质量的COPD数据集成和建模.
- 促进数字健康应用在COPD管理中的进步.
主要方法:
- 从COPD患者收集和预处理现实世界的临床数据,解决数据质量问题.
- 设计并实施了一个名为COPD平均联合学习 (COPD_AVG_FL) 的联合学习架构,使用FedAvg算法.
- 开发了一个集中式机器学习 (CML) 模型,用于对COPD_AVG_FL系统进行比较评估.
主要成果:
- COPD_AVG_FL系统在测试数据上的诊断性能显著改善,准确度增加了13.4%,精度增加了13.3%,回忆增加了12.8%,F1-Score增加了13.1%,AUC增加了12.9%.
- 联合学习方法成功地将模型培训与原始患者数据脱,确保了强大的隐私保护.
- 该系统有助于各种COPD数据的安全整合,从而产生更全面,更准确的诊断模型.
结论:
- COPD_AVG_FL有效地解决了COPD诊断中分散数据和患者隐私的挑战.
- 该系统显著提高了诊断指标,促进了AI在COPD临床环境中的实际应用.
- 这种方法为安全有效的数字健康解决方案在治疗慢性呼吸道疾病方面铺平了道路.
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