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首页> 外文会议>2011 First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology >Mining Electronic Medical Records to Explore the Linkage between Healthcare Resource Utilization and Disease Severity in Diabetic Patients
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Mining Electronic Medical Records to Explore the Linkage between Healthcare Resource Utilization and Disease Severity in Diabetic Patients

机译：挖掘电子病历以探索糖尿病患者医疗资源利用与疾病严重性之间的联系

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Knowledge discovery in electronic health records (EHRs) is a central aspect for improved clinical decision making, prognosis, and patient management. While EHRs show great promise towards better data integration, automated access, and clinical workflow improvement, the vast information they capture over time pose challenges not only for medical practitioners, but also for the information analysis by machines. The objective of this paper is to promote and emphasize the importance of exploratory analytics that are commensurate with human capabilities and constraints. Within this realm we present a novel temporal event matrix representation and learning framework that discovers complex latent event patterns, which are easily interpretable by humans. We demonstrate our framework on synthetic data and on EHRs together with an extensive validation involving over 70,000 computed latent factor models. The present study is the first to link temporal patterns of healthcare resource utilization (HRU) against a diabetic disease complications severity index to better understand the relationships between disease severity and care delivery.
机译：电子病历（EHR）中的知识发现是改善临床决策，预后和患者管理的重要方面。电子病历显示出对更好的数据集成，自动访问和临床工作流程改善的巨大希望，但随着时间的推移，它们捕获的大量信息不仅给医疗从业者带来了挑战，而且也给机器信息分析带来了挑战。本文的目的是促进和强调与人类能力和约束相称的探索性分析的重要性。在这个领域中，我们提出了一种新颖的时间事件矩阵表示和学习框架，该框架发现了复杂的潜在事件模式，这些模式很容易被人解释。我们展示了我们的综合数据和EHR框架，并进行了涉及70,000多个计算的潜在因子模型的广泛验证。本研究是第一个将医疗资源利用（HRU）的时间模式与糖尿病疾病并发症严重程度指数相关联，以更好地了解疾病严重程度与医疗服务之间的关系的方法。

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