通过持续预测反复发生的临床行为提供决策支持
Michal Weisman Raymond1, Yuval Shahar1
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Studies in health technology and informatics
|June 30, 2023
概括
这项研究引入了一个新的框架,用于使用时间模式挖掘预测反复发生的临床行为. 这种方法通过分析患者数据随时间推移来提高临床决策支持,以预测未来的事件,如低血糖症.
科学领域:
- * 临床信息学 临床信息学
- * 数据科学是一门数据科学.
- * 预测建模 * 预测建模
背景情况:
- *电子健康记录 (EHR) 包含患者的纵向数据.
- * 经常出现的临床事件需要持续监测和预测.
- *现有的决策支持系统可能无法充分利用时间模式.
研究的目的:
- * 提出一个持续预测复发性临床反应的框架.
- * 通过时间数据挖掘来增强临床决策支持系统.
- * 证明框架在预测重症监护事件中的实用性.
主要方法:
- *抽象原始的,时间标记的患者数据到有意义的间隔.
- * 将患者的时间线划分为离散的时间窗口.
- * 应用频繁的时间模式挖掘来识别重复的序列.
- *利用发现的模式作为预测模型的特征.
主要成果:
- *成功开发并应用了一个时间模式挖掘的框架.
- * 证明了框架在预测反复发生事件方面的有效性.
- *对重症监护室 (ICU) 的低血糖,低血糖和低血压预测数据的方法进行了验证.
结论:
- * 拟议的框架提供了一种可靠的方法来预测反复发生的临床反应.
- * 时间模式挖掘可以显著提高临床决策支持的准确性.
- *这种方法有可能提高关键环境中的患者护理.
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