Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Digital Me can be defined as a service that reflects the user's goals, measures, predicts, and evaluates the individual's status, and recommends actions to improve the status. This paper explores the architecture and algorithms for predicting user status and recommending actions, using only the user's data. It aims to minimize the reliance on domain-dependent models while ensuring the sustainable and privacy-preserving accumulation of data. To validate the architecture and the algorithms, we used EdNet data in education and MIMIC-III data in healthcare. We developed algorithms to recommend activities that optimize the achievement of users' goals. These algorithms operate on a simple principle applicable to general Digital Me services: recommend actions most likely to improve the user's next state, based on their likelihood of success and the expected score. We also demonstrate the feasibility of creating effective health prediction algorithms using personal federated learning, which avoids centralizing sensitive health data by storing it directly on individual devices or in private clouds.
Recommended Citation
Lee, Kyoung Jun; Jeong, Baek; Kim, Youngchan; and Kim, Suhyeon, "Towards Minimally Domain-Dependent and Privacy-Preserving Architecture and Algorithms for Digital Me Services: EdNet and MIMIC-III Experiments" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/personal_analytics/2
Towards Minimally Domain-Dependent and Privacy-Preserving Architecture and Algorithms for Digital Me Services: EdNet and MIMIC-III Experiments
Hilton Waikoloa Village, Hawaii
Digital Me can be defined as a service that reflects the user's goals, measures, predicts, and evaluates the individual's status, and recommends actions to improve the status. This paper explores the architecture and algorithms for predicting user status and recommending actions, using only the user's data. It aims to minimize the reliance on domain-dependent models while ensuring the sustainable and privacy-preserving accumulation of data. To validate the architecture and the algorithms, we used EdNet data in education and MIMIC-III data in healthcare. We developed algorithms to recommend activities that optimize the achievement of users' goals. These algorithms operate on a simple principle applicable to general Digital Me services: recommend actions most likely to improve the user's next state, based on their likelihood of success and the expected score. We also demonstrate the feasibility of creating effective health prediction algorithms using personal federated learning, which avoids centralizing sensitive health data by storing it directly on individual devices or in private clouds.
https://aisel.aisnet.org/hicss-58/da/personal_analytics/2