跨人口队伍的持续学习与分布转移:从多队列代谢综合征识别的见解
Chang Liu1, Zhangdaihong Liu2, Jingjing Liu1
1School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
概括
持续学习 (CL) 有效地适应深度学习 (DL) 模型,用于在医院和非医院环境中识别代谢综合征 (MetS),克服灾难性遗忘和提高绩效.
科学领域:
- 医疗保健中的人工智能
- 机器学习用于医学诊断
- 深度学习模型适应
背景情况:
- 深度学习 (DL) 模型在医院和非医院数据之间的分布转移中扎,导致对代谢综合征 (MetS) 等疾病的误诊.
- 在将DL模型适应新数据环境时,灾难性遗忘是关键的挑战.
研究的目的:
- 展示持续学习 (CL) 策略的潜力,以提高DL模型在各种医疗保健环境中识别MetS的性能.
- 评估CL在减轻灾难性遗忘和在分配转移下保持预测准确性的有效性.
主要方法:
- 利用来自医院 (MIMIC) 和非医院 (NHANES,专有) 的三个医疗保健数据集.
- 开发了一个 MetS 识别管道,结合了 CL 策略.
- 基于ROC曲线 (AUROC) 和精度回忆曲线 (AUC-PR) 下的累积面积的评估性能.
主要成果:
- 临床治疗方法显著优于没有临床治疗策略的顺序训练的对照组.
- 在组合测试组中获得了0.85的累积AUROC和0.65的AUC-PR.
- 培训订单对绩效产生了重大影响,医院对非医院培训产生了7.6%的AUROC改进.
结论:
- 在不同医疗保健环境中,特别是在医院和非医院环境之间,CL显示出适应DL模型的显著前景.
- 建议的CL模型有效地减少了灾难性遗忘,提高了DL模型的性能和可扩展性.
- 培训顺序是考虑在医疗应用中最优化CL模型部署的关键因素.
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