Applying Latest Data Science Technology in Cancer Screening Programs

Authors

  • Lian Wen Institute for Integrated and Intelligent Systems, Griffith University
  • Wuqi Qiu Institute of Medical Information, Chinese Academy of Medical Sciences
  • Kedian Mu School of Mathematical Sciences, Peking University

DOI:

https://doi.org/10.37256/ccds.112020445

Keywords:

cancer screening program, data science, big data, artificial intelligence, machine learning, medical informatics, ontology

Abstract

Cancer screening programs have been implemented in many different countries for many years to collect information of the fatal diseases, to provide early diagnosis, to support medical research, and to help governments making policies. However, few of those programs have utilized latest data science technologies, therefore not be able to generate the maximum benefits from those programs. To overcome this problem and improve the quality of cancer screening programs, this paper firstly (i) reviews the typical architecture and IT technologies used in current screening programs and recognizes their limitations; then (ii) introduces recent developments in data science that could be implemented in screening programs; finally (iii) proposes the structure of General Medical Screening Framework (GMSF), which could be developed to host future cancer screening programs that will advance data coverage, data accuracy, data usage and lower in the costs. The structure of GMSF and its key elements are described in this paper and some practical approaches to build GMSF are discussed. This work might initialize a series or research to bring the latest IT technologies, particularly data science technologies, into cancer screening programs, and significantly increase the efficiency and reduce the cost of future cancer screening programs.

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Published

2020-07-02

How to Cite

1.
Wen L, Qiu W, Mu K. Applying Latest Data Science Technology in Cancer Screening Programs. Cloud Computing and Data Science [Internet]. 2020 Jul. 2 [cited 2024 Mar. 29];1(1):31-9. Available from: https://ojs.wiserpub.com/index.php/CCDS/article/view/445