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.

Original languageEnglish
Pages (from-to)44-51
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Volume127
DOIs
Publication statusPublished - Apr 2016

Fingerprint

Animation
Visualization
Neoplasms
Health insurance
Oncology
Data visualization
Big data
Comorbidity
Inspection
Trajectories
Delivery of Health Care
National Health Programs
Taiwan

Keywords

  • Journal Article
  • Research Support, Non-U.S. Gov't

Cite this

Cancer-disease associations : A visualization and animation through medical big data. / Usman, Iqbal; Hsu, Chun-Kung; Nguyen, Phung Anh Alex; Clinciu, Daniel Livius; Lu, Richard; Shabbir, Syed Abdul; Yang, Hsuan-Chia; Wang, Yao-Chin; Huang, Chu-Ya; Huang, Chih-Wei; Chang, Yo-Cheng; Hsu, Min-Huei; Jian, Wen-Shan; Li, Yu-Chuan Jack.

In: Computer Methods and Programs in Biomedicine, Vol. 127, 04.2016, p. 44-51.

Research output: Contribution to journalArticle

@article{0217fafa6f774676a5192d9feb4cbdea,
title = "Cancer-disease associations: A visualization and animation through medical big data",
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.",
keywords = "Journal Article, Research Support, Non-U.S. Gov't",
author = "Iqbal Usman and Chun-Kung Hsu and Nguyen, {Phung Anh Alex} and Clinciu, {Daniel Livius} and Richard Lu and Shabbir, {Syed Abdul} and Hsuan-Chia Yang and Yao-Chin Wang and Chu-Ya Huang and Chih-Wei Huang and Yo-Cheng Chang and Min-Huei Hsu and Wen-Shan Jian and Li, {Yu-Chuan Jack}",
note = "Copyright {\circledC} 2016. Published by Elsevier Ireland Ltd.",
year = "2016",
month = "4",
doi = "10.1016/j.cmpb.2016.01.009",
language = "English",
volume = "127",
pages = "44--51",
journal = "Computer Methods and Programs in Biomedicine",
issn = "0169-2607",
publisher = "Elsevier Ireland Ltd",

}

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

VL - 127

SP - 44

EP - 51

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

SN - 0169-2607

ER -