在临床大数据中探索患者药物坚持和数据挖掘方法:当代审查
Yixian Xu1, Xinkai Zheng2, Yuanjie Li3
1Department of Anesthesiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
机器学习和电子健康记录 (EHR) 和索赔数据库的数据挖掘改善了患者的药物服药. 分析这些大数据可以提高临床决策和患者护理质量.
科学领域:
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 患者药物坚持数据越来越多地从索赔数据库和电子健康记录 (EHR) 中整合起来.
- 数据挖掘,特别是机器学习 (ML),对于从这些数据中提取可操作的见解至关重要.
- 不遵守会增加健康风险和医疗费用.
研究的目的:
- 审查ML技术在分析EHR数据中对药物坚持的应用.
- 探索医疗数据库的结构,相关的药物坚持.
- 利用监督和无监督的ML范式来研究遵守及其后果.
主要方法:
- 综合文献综述使用ML对药物坚持中的EHR应用.
- 对医疗数据库结构进行分析,以获取依从性数据.
- 监督和无监督ML技术的应用,以遵守数据.
主要成果:
- 医疗数据库 (例如,SEER,NHANES) 和ML对于临床大数据中的药物坚持分析是有效的.
- ML有助于挖掘患者的药物记录,以获得坚持的见解.
- 这些发现支持临床决策,风险分层和研究.
结论:
- 先进的数据挖掘和ML已经改变了药物坚持性研究,改善了患者的护理.
- 个性化干预和研究是推进治疗方法的关键.
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