发现和诊断患者事件流中的行为转变

W. N. Robinson, A. Akhlaghi, Tianjie Deng, Ali Raza Syed
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引用次数: 14

摘要

认知障碍用户使用辅助技术(AT)作为临床治疗计划的一部分。随着AT接口的操作,数据流挖掘技术被用于监控用户目标。在这种情况下,实时数据挖掘有助于临床医生在试图实现其目标时跟踪用户行为。随着用户学习他或她的个性化电子邮件系统,流挖掘模型上的质量度量可以识别用户目标实现的潜在变化。当一些数据挖掘模型的质量与附近的模型有显著差异时(由质量度量定义),用户的行为就会被标记为一个显著的行为变化。然后通过不同的数据挖掘决策树模型来表征用户行为的具体变化。本文描述了模型质量监测和决策树差异如何帮助识别和诊断通过电子邮件进行认知康复的案例研究中的行为变化。该技术可能更广泛地适用于其他实时数据密集型分析问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovery and diagnosis of behavioral transitions in patient event streams
Users with cognitive impairments use assistive technology (AT) as part of a clinical treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, real-time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When the quality of some data-mined models varies significantly from nearby models—as defined by quality metrics—the user's behavior is then flagged as a significant behavioral change. The specific changes in user behavior are then characterized by differencing the data-mined decision tree models. This article describes how model quality monitoring and decision tree differencing can aid in recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The technique may be more widely applicable to other real-time data-intensive analysis problems.
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