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首页> 外文期刊>Journal of biomedical informatics. >Infobuttons and classification models: a method for the automatic selection of on-line information resources to fulfill clinicians' information needs.
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Infobuttons and classification models: a method for the automatic selection of on-line information resources to fulfill clinicians' information needs.

机译：信息按钮和分类模型：一种自动选择在线信息资源以满足临床医生信息需求的方法。

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OBJECTIVE: Infobuttons are decision support tools that offer links to information resources based on the context of the interaction between a clinician and an electronic medical record (EMR) system. The objective of this study was to explore machine learning and web usage mining methods to produce classification models for the prediction of information resources that might be relevant in a particular infobutton context. DESIGN: Classification models were developed and evaluated with an infobutton usage dataset. The performance of the models was measured and compared with a reference implementation in a series of experiments. MEASUREMENTS: Level of agreement (kappa) between the models and the resources that clinicians actually used in each infobutton session. RESULTS: The classification models performed significantly better than the reference implementation (p<.0001). The performance of these models tended to decrease over time, probably due to a phenomenon known as concept drift. However, the performance of the models remained stable when concept drift handling techniques were used. CONCLUSIONS: The results suggest that classification models are a promising method for the prediction of information resources that a clinician would use to answer patient care questions.
机译：目的：信息按钮是决策支持工具，可基于临床医生和电子病历（EMR）系统之间的交互上下文提供指向信息资源的链接。这项研究的目的是探索机器学习和网络使用挖掘方法，以产生用于预测可能在特定信息按钮上下文中相关的信息资源的分类模型。设计：使用信息按钮用法数据集开发和评估分类模型。测量了模型的性能，并在一系列实验中将其与参考实现进行了比较。测量：模型与临床医生在每个信息按钮会话中实际使用的资源之间的协议（kappa）级别。结果：分类模型的性能明显优于参考实现（p <.0001）。这些模型的性能往往会随着时间的流逝而下降，这可能是由于一种被称为概念漂移的现象所致。但是，使用概念漂移处理技术时，模型的性能保持稳定。结论：结果表明分类模型是一种有前途的方法，用于预测临床医生将用来回答患者护理问题的信息资源。

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