Generating Querying Code from Text for Multi-Modal Electronic Health Record

ACL ARR 2025 February Submission6829 Authors

Abstract: Electronic health records (EHR) contain extensive structured and unstructured data, including tabular information and free-text clinical notes. Querying relevant patient information often requires complex database operations, increasing the workload for clinicians. However, complex table relationships and professional terminology in EHRs limit the query accuracy. In this work, we construct a publicly available dataset, TQGen, that integrates both \textbf{T}ables and clinical \textbf{T}ext for natural language-to-query \textbf{Gen}eration. To address the challenges posed by complex medical terminology and diverse types of questions in EHRs, we designed a medical knowledge module and a questions template matching module. For processing medical text, we introduced the concept of a toolset, which encapsulates the text processing module as a callable tool, thereby improving processing efficiency and flexibility. We conducted extensive experiments to assess the effectiveness of our dataset and workflow, demonstrating their potential to enhance information querying in EHR systems. We will release the project code after our paper is accepted.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: electronic health records, text to query generation, medical question answering

Contribution Types: Model analysis & interpretability, Data resources, Data analysis

Languages Studied: english

Submission Number: 6829