Digitization of the Individual – Personal Decision Analytics
Permanent URI for this collection
Browse
Recent Submissions
Item Unlocking The Potential of Pre-Eclampsia Self-Management System: Analysing Benefits, Challenges and Gaps(2025-01-07) Chung, Claris; Sreeprakash, AshithaPre-eclampsia is a severe hypertension condition that complicates approximately up to 10% of pregnancies. Timely diagnosis and treatment are essential since this condition poses serious threats to the health of the mother and fetus. Self-Monitoring Blood Pressure (SMBP) has become a viable approach to improve health outcomes and early diagnosis. Through a systematic literature review using PRISMA and applying the Technology Acceptance Model (TAM) as a lens, this research identifies key functionalities for developing Pre-eclampsia Self-Management Systems. Patients and health providers received improved patient-provider communication, real-time alerts, and integration with electronic health records are beneficial functionalities while expressing challenges, including financial barriers, technical literacy, and data privacy concerns. The results show the need for specialized functions to manage pre-eclampsia and emphasize the importance of considering the specific needs of patients and providers.Item Towards Minimally Domain-Dependent and Privacy-Preserving Architecture and Algorithms for Digital Me Services: EdNet and MIMIC-III Experiments(2025-01-07) Lee, Kyoung Jun; Jeong, Baek; Kim, Youngchan; Kim, SuhyeonDigital 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.Item Introduction to the Minitrack on Digitization of the Individual – Personal Decision Analytics(2025-01-07) Hong, Yvonne; Sundaram, David; Chung, Claris