论文标题
使用动态和多顾问过滤从电子健康记录中提高信息检索
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering
论文作者
论文摘要
由于有关单个患者的信息的快速增长，大多数医生在审查健康信息技术系统中的患者信息时会遭受信息过载。在本手稿中，我们提出了一种新型的混合动力和多核试验过滤方法，以改善电子健康记录的信息检索。此方法建议在患者就诊期间，来自医师的电子健康记录中的相关信息。它使用Markov模型对信息搜索动态进行建模。它还利用源自推荐系统的协作过滤的关键思想，以根据医生，患者和信息项目之间各种相似性来确定信息的优先级。我们使用印第安纳州患者护理网络的真实电子健康记录数据测试了这种新方法。我们的实验结果表明，对于46.7％的测试案例，这种新方法能够在医生真正感兴趣的前5个建议中正确确定相关信息的优先级。
Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscript, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records for physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, to prioritize information based on various similarities among physicians, patients and information items. We tested this new method using real electronic health record data from the Indiana Network for Patient Care. Our experimental results demonstrated that for 46.7% of testing cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in.
