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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZOFFSETFROM:+0900
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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Seoul:20260626T100000
DTEND;TZID=Asia/Seoul:20260626T120000
DTSTAMP:20260610T084619
CREATED:20260528T012227Z
LAST-MODIFIED:20260528T012227Z
UID:12546-1782468000-1782475200@www.ibs.re.kr
SUMMARY:Learning Longitudinal Health Representations from EHR and Wearable Data - Hyunji Jeong
DESCRIPTION:In this talk\, we discuss the paper “Learning Longitudinal Health Representations from EHR and Wearable Data” by Yuanyun Zhang et al.\, arXiv\, 2026. \nAbstract: \nFoundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable devices provide dense continuous physiological signals but lack semantic grounding. Existing methods usually model these data sources separately or combine them through late fusion. We propose a multimodal foundation model that jointly represents electronic health records and wearable data as a continuous time latent process. The model uses modality specific encoders and a shared temporal backbone pretrained with self supervised and cross modal objectives. This design produces representations that are temporally coherent and clinically grounded. Across forecasting physiological and risk modeling tasks the model outperforms strong electronic health record only and wearable only baselines especially at long horizons and under missing data. These results show that joint electronic health record and wearable pretraining yields more faithful representations of longitudinal health.
URL:https://www.ibs.re.kr/bimag/event/learning-longitudinal-health-representations-from-ehr-and-wearable-data-hyunji-jeong/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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