Personalizing Public Health: Using Deep Q-Learning to Lower Type II Diabetes Risk in India

dc.contributor.advisorThanh H. Nguyen
dc.contributor.advisorMichal Young
dc.contributor.authorReis, Sabrina
dc.date.accessioned2023-09-19T22:53:54Z
dc.date.available2023-09-19T22:53:54Z
dc.date.issued2023-03
dc.description56 pagesen_US
dc.description.abstractThe high prevalence of type II diabetes in India necessitates novel public health tools to promote behaviors that lower the likelihood of developing diabetes and associated comorbidities. We introduce a public health text messaging system that learns from participant feedback to target each individual with messages that are most relevant to their current health behaviors. The participants who completed the AI-assisted text messaging intervention outperformed the control group in three out of eight categories. Highly engaged participants outperformed the control group in four out of eight categories and outperformed moderately engaged participants in seven out of eight categories, suggesting that engagement with the intervention is positively correlated with behavior change. We also perform a demographic analysis of the intervention results along the lines of sex, education level, and age. Recommendations for future work include personalizing message timing to increase intervention engagement and leveraging the plethora of data that is generated by the AI-assisted approach.en_US
dc.identifier.urihttps://hdl.handle.net/1794/28895
dc.language.isoenen_US
dc.publisherUniversity of Oregonen_US
dc.rightsCreative Commons BY-NC-ND 4.0-USen_US
dc.subjectartificial intelligenceen_US
dc.subjectAIen_US
dc.subjectmachine learningen_US
dc.subjectAI for social gooden_US
dc.subjectpublic healthen_US
dc.subjecttype II diabetes in Indiaen_US
dc.titlePersonalizing Public Health: Using Deep Q-Learning to Lower Type II Diabetes Risk in Indiaen_US
dc.typeThesis / Dissertationen_US

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