Next Article in Journal
Economic Evaluation of Hepatitis C Treatment Extension to Acute Infection and Early-Stage Fibrosis Among Patients Who Inject Drugs in Developing Countries: A Case of China
Next Article in Special Issue
Analysis of Network Structure and Doctor Behaviors in E-Health Communities from a Social-Capital Perspective
Previous Article in Journal
A Spatial-Temporal Resolved Validation of Source Apportionment by Measurements of Ambient VOCs in Central China
Previous Article in Special Issue
Methodological Quality of Manuscripts Reporting on the Usability of Mobile Applications for Pain Assessment and Management: A Systematic Review
Open AccessArticle

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

1
Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, Tokyo 152-8850, Japan
2
Cancer Scan, Co., Ltd., Tokyo 141-0031, Japan
3
Life Style by Design Research Unit, Institute for Future Initiatives, the University of Tokyo, Tokyo 113-0033, Japan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(3), 790; https://doi.org/10.3390/ijerph17030790
Received: 2 December 2019 / Revised: 21 January 2020 / Accepted: 23 January 2020 / Published: 28 January 2020
(This article belongs to the Special Issue E-Health Services)
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. View Full-Text
Keywords: disease prevention; machine learning; nudge theory; medical data; social impact bond disease prevention; machine learning; nudge theory; medical data; social impact bond
Show Figures

Figure 1

MDPI and ACS Style

Misawa, D.; Fukuyoshi, J.; Sengoku, S. Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond. Int. J. Environ. Res. Public Health 2020, 17, 790.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop