Chih-Wei Huang1, Shabbir Syed-Abdul1, Wen-Shan Jian2, Usman Iqbal1, Phung-Anh Alex Nguyen1, Peisan Lee3, Shen-Hsien Lin1, Wen-Ding Hsu4, Mai-Szu Wu5, Chun-Fu Wang6, Kwan-Liu Ma7, Yu-Chuan Jack Li8. 1. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan. 2. School of Health Care Administration, Taipei Medical University, Taipei, Taiwan Faculty of Health Sciences, Macau University of Science and Technology, Macau. 3. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan Institute of Biomedical Informatics, National Yang Ming University, Taipei, Taiwan. 4. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan Department of Nephrology, New Taipei City Hospital, New Taipei City Government, Taipei, Taiwan. 5. Division of Nephrology, Taipei Medical University Hospital, Taipei, Taiwan School of Medicine, Taipei Medical University, Taipei, Taiwan. 6. Department of Computer Science, University of California-Davis, California, USA. 7. Department of Computer Science, University of California-Davis, California, USA ma@cs.ucdavis.edu jack@tmu.edu.tw. 8. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan Department of Dermatology, Wan-Fang Hospital, Taipei, Taiwan. ma@cs.ucdavis.edu jack@tmu.edu.tw.
Abstract
OBJECTIVE: The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization. MATERIALS AND METHODS: Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011. From this group, those patients diagnosed with CKD were included in the analysis. We selected 11 of the most common diseases associated with CKD before its diagnosis and followed them until their death or up to 2011. We used a Sankey-style diagram, which quantifies and visualizes the transition between pre- and post-CKD states with various lines and widths. The line represents groups and the width of a line represents the number of patients transferred from one state to another. RESULTS: The patients were grouped according to their states: that is, diagnoses, hemodialysis/transplantation procedures, and events such as death. A Sankey diagram with basic zooming and planning functions was developed that temporally and qualitatively depicts they had amid change of comorbidities occurred in pre- and post-CKD states. DISCUSSION: This represents a novel visualization approach for temporal patterns of polymorbidities associated with any complex disease and its outcomes. The Sankey diagram is a promising method for visualizing complex diseases and exploring the effect of comorbidities on outcomes in a time-evolution style. CONCLUSIONS: This type of visualization may help clinicians foresee possible outcomes of complex diseases by considering comorbidities that the patients have developed.
OBJECTIVE: The aim of this study is to analyze and visualize the polymorbidity associated with chronic kidney disease (CKD). The study shows diseases associated with CKD before and after CKD diagnosis in a time-evolutionary type visualization. MATERIALS AND METHODS: Our sample data came from a population of one million individuals randomly selected from the Taiwan National Health Insurance Database, 1998 to 2011. From this group, those patients diagnosed with CKD were included in the analysis. We selected 11 of the most common diseases associated with CKD before its diagnosis and followed them until their death or up to 2011. We used a Sankey-style diagram, which quantifies and visualizes the transition between pre- and post-CKD states with various lines and widths. The line represents groups and the width of a line represents the number of patients transferred from one state to another. RESULTS: The patients were grouped according to their states: that is, diagnoses, hemodialysis/transplantation procedures, and events such as death. A Sankey diagram with basic zooming and planning functions was developed that temporally and qualitatively depicts they had amid change of comorbidities occurred in pre- and post-CKD states. DISCUSSION: This represents a novel visualization approach for temporal patterns of polymorbidities associated with any complex disease and its outcomes. The Sankey diagram is a promising method for visualizing complex diseases and exploring the effect of comorbidities on outcomes in a time-evolution style. CONCLUSIONS: This type of visualization may help clinicians foresee possible outcomes of complex diseases by considering comorbidities that the patients have developed.
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