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.
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.
Recent advancements in multimodal foundation models have showcased impressive capabilities in understanding and reasoning with visual and textual information. Adapting these foundation models trained for general usage to specialized domains like biomedicine requires large-scale domain-specific instruction datasets.
Graphs data is crucial for many applications, and much of it exists in the relations described in textual format. As a result, being able to accurately recall and encode a graph described in earlier text is a basic yet pivotal ability that LLMs need to demonstrate if they are to perform reasoning tasks that involve graph-structured information.
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and challenging to obtain.
Clinical natural language processing requires methods that can address domainspecific challenges, such as complex medical terminology and clinical contexts. Recently, large language models (LLMs) have shown promise in this domain. Yet, their direct deployment can lead to privacy issues and are constrained by resources.