Laboratory tests generate structured numerical data, which a clinician must interpret to justify diagnoses and help patients understand the outcomes of the tests. LLMs have the potential to assist with the generation of interpretative comments, but legitimate concerns remain about the accuracy and reliability of the generation process. This work introduces LAB-KG, which conditions the generation process of an LLM on information retrieved from a knowledge graph of relevant patient conditions and lab test results. This helps to ground the text-generation process in accurate medical knowledge and enables generated text to be traced back to the knowledge graph. Given a dataset of laboratory test results and associated interpretive comments, we show how an LLM can build a KG of the relationships between laboratory test results, reference ranges, patient conditions and demographic information. We further show that the interpretive comments produced by an LLM conditioned on information retrieved from the KG are of higher quality than those from a standard RAG method. Finally, we show how our KG approach can improve the interpretability of the LLM generated text.

The BioNLP ACL’24 Shared Task on Streamlining Discharge Documentation aims to reduce the administrative burden on clinicians by automating the creation of critical sections of patient discharge letters. This paper presents our approach using the Llama3 8B quantized model to generate the “Brief Hospital Course” and “Discharge Instructions” sections. We employ a zero-shot method combined with Retrieval-Augmented Generation (RAG) to produce concise, contextually accurate summaries. Our contributions include the development of a curated template-based approach to ensure reliability and consistency, as well as the integration of RAG for word count prediction. We also describe several unsuccessful experiments to provide insights into our pathway for the competition. Our results demonstrate the effectiveness and efficiency of our approach, achieving high scores across multiple evaluation metrics.