Cancer-disease associations: A visualization and animation through medical big data. Issue 127 (April 2016) - Iqbal, Usman; Hsu, Chun-Kung; Nguyen, Phung Anh (Alex); Clinciu, Daniel Livius; Lu, Richard; Syed-Abdul, Shabbir; Yang, Hsuan-Chia; Wang, Yao-Chin; Huang, Chu-Ya; Huang, Chih-Wei; Chang, Yo-Cheng; Hsu, Min-Huei; et al.
Highlights: A novel approach for visualization of temporal patterns focused on the association of cancers with other diseases. A dynamic animation of cancer-disease association across different age groups and gender. Identifying comorbidity relationships and providing more information for medical researchers. 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 athttp://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.