Usman Iqbal1, Chun-Kung Hsu1, Phung Anh Alex Nguyen1, Daniel Livius Clinciu1, Richard Lu1, Shabbir Syed-Abdul1, Hsuan-Chia Yang2, Yao-Chin Wang3, Chu-Ya Huang3, Chih-Wei Huang1, Yo-Cheng Chang3, Min-Huei Hsu4, Wen-Shan Jian5, Yu-Chuan Jack Li6. 1. Graduate of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taiwan. 2. Graduate of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taiwan; Institute of Biomedical Informatics, National Yang Ming University, Taiwan. 3. Graduate of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan. 4. Graduate of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taiwan; Bureau of International Cooperation, Ministry of Health and Welfare, Taipei, Taiwan. 5. Graduate of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taiwan; School of Health Care Administration, Taipei Medical University, Taipei, Taiwan; Faculty of Health Sciences, Macau University of Science and Technology, Macau, China. 6. Graduate of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taiwan; Department of Dermatology, Taipei Medical University - Wan Fang Hospital, Taipei, Taiwan. Electronic address: jaak88@gmail.com.
Abstract
OBJECTIVE: Cancer is the primary disease responsible for death and disability worldwide. Currently, prevention and early detection represents the best hope for cure. Knowing the expected diseases that occur with a particular cancer in advance could lead to physicians being able to better tailor their treatment for cancer. The aim of this study was to build an animated visualization tool called as Cancer Associations Map Animation (CAMA), to chart the association of cancers with other disease over time. METHODS: The study population was collected from the Taiwan National Health Insurance Database during the period January 2000 to December 2002, 782 million outpatient visits were used to compute the associations of nine major cancers with other diseases. A motion chart was used to quantify and visualize the associations between diseases and cancers. RESULTS: The CAMA motion chart that was built successfully facilitated the observation of cancer-disease associations across ages and genders. The CAMA system can be accessed online at http://203.71.86.98/web/runq16.html. CONCLUSION: The CAMA animation system is an animated medical data visualization tool which provides a dynamic, time-lapse, animated view of cancer-disease associations across different age groups and gender. Derived from a large, nationwide healthcare dataset, this exploratory data analysis tool can detect cancer comorbidities earlier than is possible by manual inspection. Taking into account the trajectory of cancer-specific comorbidity development may facilitate clinicians and healthcare researchers to more efficiently explore early stage hypotheses, develop new cancer treatment approaches, and identify potential effect modifiers or new risk factors associated with specific cancers.
OBJECTIVE:Cancer is the primary disease responsible for death and disability worldwide. Currently, prevention and early detection represents the best hope for cure. Knowing the expected diseases that occur with a particular cancer in advance could lead to physicians being able to better tailor their treatment for cancer. The aim of this study was to build an animated visualization tool called as Cancer Associations Map Animation (CAMA), to chart the association of cancers with other disease over time. METHODS: The study population was collected from the Taiwan National Health Insurance Database during the period January 2000 to December 2002, 782 million outpatient visits were used to compute the associations of nine major cancers with other diseases. A motion chart was used to quantify and visualize the associations between diseases and cancers. RESULTS: The CAMA motion chart that was built successfully facilitated the observation of cancer-disease associations across ages and genders. The CAMA system can be accessed online at http://203.71.86.98/web/runq16.html. CONCLUSION: The CAMA animation system is an animated medical data visualization tool which provides a dynamic, time-lapse, animated view of cancer-disease associations across different age groups and gender. Derived from a large, nationwide healthcare dataset, this exploratory data analysis tool can detect cancer comorbidities earlier than is possible by manual inspection. Taking into account the trajectory of cancer-specific comorbidity development may facilitate clinicians and healthcare researchers to more efficiently explore early stage hypotheses, develop new cancer treatment approaches, and identify potential effect modifiers or new risk factors associated with specific cancers.
Authors: Taher M Ghazal; Sajid Hussain; Muhammad Farhan Khan; Muhammad Adnan Khan; Raed A T Said; Munir Ahmad Journal: Comput Intell Neurosci Date: 2022-03-24