Supervised prediction of post-stroke upper limb motor recovery with uncertain knowledge graph and large language model

Tianxing Wu, Xixi Wu, Jian Li, Hangyu Sha, Haofen Wang, Ya Chen, Yali Jiang, Li Liu, Yue Lan, Xiao Lu

Abstract: Accurate prediction of post-stroke upper limb motor recovery is crucial for developing stroke rehabilitation strategies. However, existing methods often focus on static predictions at a fixed post-stroke time point and fail to utilize unstructured textual data within electronic health records (EHRs). To overcome both limitations, we propose a new task which is to dynamically predict the recovery outcomes at variable time points based on the most recent EHR, and aim to address this task by leveraging the text in EHRs with uncertain knowledge graph (UKG) and large language model (LLM).

External IDs:dblp:journals/hisas/WuWLSWCJLLL25