IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i3p790-d313644.html
   My bibliography  Save this article

Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond

Author

Listed:
  • Daitaro Misawa

    (Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, Tokyo 152-8850, Japan)

  • Jun Fukuyoshi

    (Cancer Scan, Co., Ltd., Tokyo 141-0031, Japan)

  • Shintaro Sengoku

    (Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, Tokyo 152-8850, Japan
    Life Style by Design Research Unit, Institute for Future Initiatives, the University of Tokyo, Tokyo 113-0033, Japan)

Abstract

There have been prior attempts to utilize machine learning to address issues in the medical field, particularly in diagnoses using medical images and developing therapeutic regimens. However, few cases have demonstrated the usefulness of machine learning for enhancing health consciousness of patients or the public in general, which is necessary to cause behavioral changes. This paper describes a novel case wherein the uptake rate for colorectal cancer examinations has significantly increased due to the application of machine learning and nudge theory. The paper also discusses the effectiveness of social impact bonds (SIBs) as a scheme for realizing these applications. During a healthcare SIB project conducted in the city of Hachioji, Tokyo, machine learning, based on historical data obtained from designated periodical health examinations, digitalized medical insurance receipts, and medical examination records for colorectal cancer, was used to deduce segments for whom the examination was recommended. The result revealed that out of the 12,162 people for whom the examination was recommended, 3264 (26.8%) received it, which exceeded the upper expectation limit of the initial plan (19.0%). We conclude that this was a successful case that stimulated discussion on potential further applications of this approach to wider regions and more diseases.

Suggested Citation

  • Daitaro Misawa & Jun Fukuyoshi & Shintaro Sengoku, 2020. "Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond," IJERPH, MDPI, vol. 17(3), pages 1-11, January.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:790-:d:313644
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/3/790/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/3/790/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nenad Tomašev & Xavier Glorot & Jack W. Rae & Michal Zielinski & Harry Askham & Andre Saraiva & Anne Mottram & Clemens Meyer & Suman Ravuri & Ivan Protsyuk & Alistair Connell & Cían O. Hughes & Alan K, 2019. "A clinically applicable approach to continuous prediction of future acute kidney injury," Nature, Nature, vol. 572(7767), pages 116-119, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rolf Wynn & Elia Gabarron & Jan-Are K. Johnsen & Vicente Traver, 2020. "Special Issue on E-Health Services," IJERPH, MDPI, vol. 17(8), pages 1-6, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cinyoung Hur & JeongA Wi & YoungBin Kim, 2020. "Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records," IJERPH, MDPI, vol. 17(22), pages 1-14, November.
    2. Elarbi Badidi, 2023. "Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions," Future Internet, MDPI, vol. 15(11), pages 1-34, November.
    3. Fruehwirt, Wolfgang & Duckworth, Paul, 2021. "Towards better healthcare: What could and should be automated?," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    4. Rabaï Bouderhem, 2024. "Shaping the future of AI in healthcare through ethics and governance," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:790-:d:313644. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.