TY - JOUR
T1 - Cancer-disease associations
T2 - A visualization and animation through medical big data
AU - Usman, Iqbal
AU - Hsu, Chun-Kung
AU - Nguyen, Phung Anh Alex
AU - Clinciu, Daniel Livius
AU - Lu, Richard
AU - Shabbir, Syed Abdul
AU - Yang, Hsuan-Chia
AU - Wang, Yao-Chin
AU - Huang, Chu-Ya
AU - Huang, Chih-Wei
AU - Chang, Yo-Cheng
AU - Hsu, Min-Huei
AU - Jian, Wen-Shan
AU - Li, Yu-Chuan Jack
N1 - Copyright © 2016. Published by Elsevier Ireland Ltd.
PY - 2016/4
Y1 - 2016/4
N2 - 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.
AB - 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.
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-84991864407&origin=resultslist&sort=plf-f&src=s&sid=8d22986e3785f1451d68ba5bfaf7c24f&sot=a&sdt=a&sl=18&s=AU-ID%2856402033000%29&relpos=9&citeCnt=10&searchTerm=
UR - https://www.scopus.com/results/citedbyresults.uri?sort=plf-f&cite=2-s2.0-84991864407&src=s&imp=t&sid=200db71f47dead89fecfc1ee18423d9f&sot=cite&sdt=a&sl=0&origin=recordpage&editSaveSearch=&txGid=91bc68a77c278ee01789add29cf5cfa1
U2 - 10.1016/j.cmpb.2016.01.009
DO - 10.1016/j.cmpb.2016.01.009
M3 - Article
C2 - 27000288
SN - 0169-2607
VL - 127
SP - 44
EP - 51
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
ER -
