Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering.
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DOI：
10.1371/journal.pone.0255467
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Schleyer T
中科院分区：
文献类型：
--
作者：
Ning X;Fan Z;Burgun E;Ren Z;Schleyer T
由于个体患者可用信息的快速增长，大多数医生在审查卫生信息技术系统中的患者信息时遭受信息过载和效率低下的困扰。本文提出了一种新的混合动态和多协同过滤方法，以提高电子病历信息的检索效率。该方法在患者就诊时向医生推荐电子健康记录中的相关信息。它使用马尔可夫模型对信息搜索动态建模。它还利用了源自推荐系统的协作过滤的关键思想，根据医生、患者和信息项目之间的各种相似性对信息进行优先级排序。我们使用来自印第安纳州患者护理网络的电子健康记录数据对这种新方法进行了测试，这是一个由印第安纳州健康信息交换中心维护的大型组织间临床数据存储库。我们的实验结果表明，对于前5名推荐，我们的方法能够正确预测医生对46.7%的测试用例感兴趣的信息。对于排名前1的推荐，相应的数字为24.7%。此外，新方法在top-1推荐方面比传统马尔可夫模型高出22.3%。
Due to the rapid growth of information available about individual patients, most physicians suffer from information overload and inefficiencies when they review patient information in health information technology systems. In this paper, 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 to 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, for prioritizing information based on various similarities among physicians, patients and information items. We tested this new method using electronic health record data from the Indiana Network for Patient Care, a large, inter-organizational clinical data repository maintained by the Indiana Health Information Exchange. Our experimental results demonstrated that, for top-5 recommendations, our method was able to correctly predict the information in which physicians were interested in 46.7% of all test cases. For top-1 recommendations, the corresponding figure was 24.7%. In addition, the new method was 22.3% better than the conventional Markov model for top-1 recommendations.
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影响因子：
4.5
作者：
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通讯作者：
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