Electronic health records (EHRs) contain rich longitudinal information essential for clinical decision-making, yet large language models (LLMs) struggle to reason across patient timelines. We introduce \textbf{TIMER} (\textbf{T}emporal \textbf{I}nstruction \textbf{M}odeling and \textbf{E}valuation for Longitudinal Clinical \textbf{R}ecords), a method to improve LLMs’ temporal reasoning over multi-visit EHRs through time-aware instruction tuning.
Objective: This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains.
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data.