基于加权关联规则的放射学临床记录挖掘

Mohammad S. Alodadi
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引用次数: 2

摘要

电子健康记录(EHR)用于记录患者的医疗状况和详细的就诊信息。电子病历的结构化部分可用于为患者的行政和财务管理服务。然而,非结构化的部分,包括应用于患者的干预措施,并以文本格式转录,由于叙事格式,有未开发的知识。与可应用于结构化数据库的传统数据挖掘技术不同,在临床记录上应用大型自动化分析可能为单个患者或患者群体的医疗决策提供更好的支持。它还允许发现新出现的关联,利用存储在EHR非结构化文本中的数据探索患者之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radiology Clinical Notes Mining Using Weighted Association Rules
Electronic health record (EHR) serves to capture the patients' medical conditions and detailed visits information. The structured part of EHR can be used to serve administrative and financial management of patients. However, the unstructured part, that contains the interventions applied to the patient and transcribed in textual format, have unexplored knowledge due to the narrative format. Unlike conventional data mining techniques that can be applied to structured databases, applying large and automated analysis on clinical notes can potentially provide better support for medical decision making for an individual patient or for collective of patients. It can also allow for discovery of emerging associations to explore relationships among the patients using the data stored in the unstructured text of the EHR.
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