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Uncovering insights from healthcare archives to improve operations: An association analysis for cervical cancer screening

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  • Lee, Carmen Kar Hang
  • Tse, Ying Kei
  • Ho, G.T.S.
  • Chung, S.H.

Abstract

The digitalisation in healthcare opens opportunities for more effective chronic disease management. Digitalised medical records are valuable data sources for identifying high-risk patients and facilitating early clinical intervention. However, the liberation of data has plagued adoption amongst physicians as massive data mean more difficult to identify important knowledge from the data. In the cervical cancer context, many patients are adherence to prescription medications only when symptoms appear, beyond the earlier point-in-time of the disease progression. Regular screening is the only way to detect abnormal cells that may develop into cancer if left untreated. Yet, without a comprehensive understanding of the relationship between risk factors and healthcare outcomes, inappropriate screening procedures may be conducted, lengthening the treatment process. Delay in the treatment process may have an irreversible influence on patients’ conditions as chronic diseases progress. This study demonstrates a data-mining framework which extracts knowledge that can advance cervical cancer screening processes in the form of association rules and improves the generalisation potential of the rules for deployment. The knowledge discovered serves as an additional supplement for physicians’ experience and uncovers appropriate screening strategies based on patients’ risk factors, increasing the chances of high-risk patients getting treated for cervical pre-cancers.

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  • Lee, Carmen Kar Hang & Tse, Ying Kei & Ho, G.T.S. & Chung, S.H., 2021. "Uncovering insights from healthcare archives to improve operations: An association analysis for cervical cancer screening," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    • Handle: RePEc:eee:tefoso:v:162:y:2021:i:c:s0040162520312014
    DOI: 10.1016/j.techfore.2020.120375
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    References listed on IDEAS

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    1. Schuitmaker, Tjerk Jan, 2012. "Identifying and unravelling persistent problems," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1021-1031.
    2. David E. Winickoff, 2006. "Genome and Nation: Iceland's Health Sector Database and its Legacy," Innovations: Technology, Governance, Globalization, MIT Press, vol. 1(2), pages 80-105, April.
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    Cited by:

    1. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    2. Basile, Luigi Jesus & Carbonara, Nunzia & Pellegrino, Roberta & Panniello, Umberto, 2023. "Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making," Technovation, Elsevier, vol. 120(C).
    3. Li, Xinwei & Xu, Mao & Zeng, Wenjuan & Tse, Ying Kei & Chan, Hing Kai, 2023. "Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).

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