부산대학교 도서관

Acute Coronary Syndrome (ACS), an emergent medical condition, is intricately linked to environmental factors like air pollution and meteorological conditions. Harnessing regional environmental data, such as weather metrics, can promptly forecast ACS incidence rates, enabling optimised medical resource allocation and increased patient recovery rates. However, the prediction task is rendered complex due to disparities in data collection capabilities across institutions, yielding datasets with analogous features but significant label variations, impeding the application of universal models. Challenges abound due to the heterogeneity of multi-factor data, temporal alignment disparities, and the intricacies of sparse data. To address these challenges, this paper introduces the Domain Adaptation with Multi-factor Associative Structures (DAMAS), a time-series domain adaptation approach based on multi-factor sparse associative frameworks. Augmented by an isomorphic attention-driven variable label mapping scheme and combined with multi-layer perceptrons, our approach skilfully negotiates label imbalances. This results in refined prediction precision connecting environmental factors to regional ACS incidences.