Leveraging A Medical Knowledge Graph into Large Language Models for
  Diagnosis Prediction
Yanjun Gao Ruizhe Li John Caskey Dmitriy Dligach Timothy Miller Matthew M. Churpek Majid Afshar
Electronic Health Records (EHRs) and routine documentation practices play a
vital role in patients' daily care, providing a holistic record of health,
diagnoses, and treatment. However, complex and verbose EHR narratives overload
healthcare providers, risking diagnostic inaccuracies. While Large Language
Models (LLMs) have showcased their potential in diverse language tasks, their
application in the healthcare arena needs to ensure the minimization of
diagnostic errors and the prevention of patient harm. In this paper, we outline
an innovative approach for augmenting the proficiency of LLMs in the realm of
automated diagnosis generation, achieved through the incorporation of a medical
knowledge graph (KG) and a novel graph model: Dr.Knows, inspired by the
clinical diagnostic reasoning process. We derive the KG from the National
Library of Medicine's Unified Medical Language System (UMLS), a robust
repository of biomedical knowledge. Our method negates the need for
pre-training and instead leverages the KG as an auxiliary instrument aiding in
the interpretation and summarization of complex medical concepts. Using
real-world hospital datasets, our experimental results demonstrate that the
proposed approach of combining LLMs with KG has the potential to improve the
accuracy of automated diagnosis generation. More importantly, our approach
offers an explainable diagnostic pathway, edging us closer to the realization
of AI-augmented diagnostic decision support systems.