对于时间敏感的医疗保健应用程序的持续适应的表示学习框架
Akash Choudhuri1, Hankyu Jang1, Alberto M Segre1
1Department of Computer Science, Iowa City, Iowa, USA.
概括
本研究介绍了医疗保健AI的持续适应性表示学习. 它使模型能够适应不断变化的数据,改善时间敏感的医疗应用中的预测.
科学领域:
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
- 人工智能的人工智能
背景情况:
- 非静态环境对机器学习模型构成挑战.
- 持续学习允许模型适应新数据,同时保留旧知识.
- 医疗保健数据是动态的,医生和药物等实体不断变化.
研究的目的:
- 为不断发展的医疗保健应用提出一个新的框架,即持续适应的代表性学习.
- 为了使机器学习表示适应变化的数据分布.
- 解决在时间敏感的医疗保健中对持续学习的探索不足的问题.
主要方法:
- 利用医疗保健实体的背景 (例如交互) 来识别和重新训练不断变化的表示.
- 从临床笔记中利用患者信息来产生强大的医疗保健嵌入.
- 为动态医疗数据制定持续学习策略.
主要成果:
- 在现实世界医疗保健数据集中不断适应的表示的证明有效性.
- 在时间敏感的任务中成功应用,如Clostridioides difficile感染发病率预测.
- 在医疗重症监护病房转移预测中展示了实用性.
结论:
- 持续适应性表示学习有效地处理非静止医疗保健数据.
- 该框架提供了实际的好处,特别是在资源较少的临床环境中.
- 通过使用临床笔记,实现了强大而准确的医疗保健嵌入.
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