文献类型：期刊文献

英文题名：Towards A Task Taxonomy of Visual Analysis of Electronic Health or Medical Record Data

作者：Bian, Xiaohui[1,2]; Kharrazi, Hadi[3]; Caban, Jesus J.[4]; He, Gaoqi[1,5]; Feng, Zhiquan[6,7]; Chen, Jian[8]

机构：[1] Department of Computer Science and Technology, East China University of Science and Technology, Shanghai, China; [2] Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD, United States; [3] Department of Health Policy and Management, Johns Hopkins School of Public Health, Baltimore, United States; [4] National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, United States; [5] Department of Computer Science and Software Engineering, East China Normal University, Shanghai, China; [6] School of Information Science and Engineering, University of Jinan, Jinan, China; [7] Shandong Provincial Key Laboratory of Network-based Intelligent Computing, Jinan, China; [8] Department of Computer Science and Engineering, Ohio State University, Columbus, United States

年份：2018

起止页码：281

外文期刊名：Proceedings of the 2018 IEEE International Conference on Progress in Informatics and Computing, PIC 2018

收录：EI(收录号：20192106964380)

语种：英文

外文关键词：Flow visualization - Data visualization - Records management - Diagnosis - eHealth - Job analysis - Data acquisition - Data mining - Medical computing

摘要：We integrate literature- and data-driven task analysis methods to derive an initial task taxonomy for electronic health record (EHR) and electronic medical record (EMR) data analysis. An EHR (EMR) is a digital and longitudinal version of a patients health(medical) information and may include all key clinical events relevant to that persons health (medical) history, such as provider, demographics, progress notes, medicine, diagnosis, etc. Our goal is to arrive a task taxonomy for analyzing EHR (EMR) datasets because tasks play an important role in the design and evaluation of visualization techniques. Our method has three stages: data collection, task modelling, and task taxonomy summary. In data collection, we first survey related literature from the past two decades and extract typical tasks and corresponding data by extracting goals and scenarios of the particular work. We introduce multiple continuous relations to describe specific binary or multiple continuous relation-seeking tasks. Finally, we arrive an initial set of task types for EHR/EMR analysis that guide the design and evaluation of visualization techniques. ? 2018 IEEE.