16 引文 (Scopus)

摘要

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.
原文英語
頁(從 - 到)44-51
頁數8
期刊Computer Methods and Programs in Biomedicine
127
DOIs
出版狀態已發佈 - 四月 2016

指紋

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

引用此文

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.

於: Computer Methods and Programs in Biomedicine, 卷 127, 04.2016, p. 44-51.

研究成果: 雜誌貢獻文章

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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.",
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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

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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.

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