Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China; Health Management Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China.
Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China; State Key Laboratory of Software Development Environment and Big Data Brain Computing Lab (SKLSDE and BDBC Lab), Beihang University, Beijing 100191, China.
Artif Intell Med. 2020 Mar;103:101782. doi: 10.1016/j.artmed.2019.101782. Epub 2019 Dec 28.
Electronic Medical Records (EMRs) contain temporal and heterogeneous doctor order information that can be used for treatment pattern discovery. Our objective is to identify "right patient", "right drug", "right dose", "right route", and "right time" from doctor order information.
We propose a fusion framework to extract typical treatment patterns based on multi-view similarity Network Fusion (SNF) method. The multi-view SNF method involves three similarity measures: content-view similarity, sequence-view similarity and duration-view similarity. An EMR dataset and two metrics were utilized to evaluate the performance and to extract typical treatment patterns.
Experimental results on a real-world EMR dataset show that the multi-view similarity network fusion method outperforms all the single-view similarity measures and also outperforms the existing similarity measure methods. Furthermore, we extract and visualize typical treatment patterns by clustering analysis.
The extracted typical treatment patterns by combining doctor order content, sequence, and duration views can provide data-driven guidelines for artificial intelligence in medicine and help clinicians make better decisions in clinical practice.
电子病历（EMR）包含时间和异质的医嘱信息，可用于发现治疗模式。我们的目标是从医嘱信息中确定“正确的患者”、“正确的药物”、“正确的剂量”、“正确的途径”和“正确的时间”。
我们提出了一种融合框架，基于多视图相似性网络融合（SNF）方法提取典型的治疗模式。多视图 SNF 方法涉及三种相似性度量：内容视图相似性、序列视图相似性和持续时间视图相似性。使用 EMR 数据集和两个指标来评估性能并提取典型的治疗模式。
在真实的 EMR 数据集上的实验结果表明，多视图相似性网络融合方法优于所有单视图相似性度量方法，也优于现有的相似性度量方法。此外，我们通过聚类分析提取和可视化典型的治疗模式。
通过结合医嘱的内容、序列和持续时间视图来提取的典型治疗模式，可以为医学人工智能提供数据驱动的指导，并帮助临床医生在临床实践中做出更好的决策。
