让不可能成为可能:为基于综合症的异质患者景观设计的概括模型
Joshua Pei Le1, Supreeth Prajwal Shashikumar2, Atul Malhotra3
1School of Medicine, University of Limerick, Castletroy, Co, Limerick V94 T9PX, Ireland.
Critical care clinics
|September 13, 2023
概括
机器学习模型在重症监护室面临挑战,原因是综合征状况和数据问题. 转移学习等先进的方法显示出改善严重病患者算法概括性的前景.
科学领域:
- 关键护理医学 关键护理医学
- 数据科学是数据科学.
- 机器学习 机器学习
背景情况:
- 综合症状况,如败血症,在重症监护室 (ICU) 很常见.
- 机器学习 (ML) 算法由于这些条件和医院特定实践而面临性能限制.
- 数据缺失对优化ML算法性能构成重大障碍.
研究的目的:
- 探索如何先进的数据科学技术可以提高ML算法对重症患者的概括性.
- 确定在重症监护机构应用 ML 的共同挑战的潜在解决方案.
主要方法:
- 审查数据科学的最新进展,包括转移学习,合规预测和持续学习.
- 讨论减轻临床数据集数据缺失的策略.
- 考虑限制ML模型外部通用性的因素.
主要成果:
- 先进的数据科学方法,如转移学习,合规预测和持续学习,可能会提高ML的概括性.
- 处理数据缺失的策略可以减轻性能障碍.
- 医院实践模式可以限制ML算法的外部有效性.
结论:
- 较新的数据科学方法有可能改善临床关怀中的ML性能.
- 需要随机试验来验证这些先进方法对以患者为中心的结果的影响.
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