Utility of word embeddings from large language models in medical diagnosis.

Objective: This study evaluates the utility of word embeddings, generated by large language models (LLMs), for medical diagnosis by comparing the semantic proximity of symptoms to their eponymic disease embedding ("eponymic condition") and the mean of all symptom embeddings associated with a disease ("ensemble mean").

Materials and methods: Symptom data for 5 diagnostically challenging pediatric diseases-CHARGE syndrome, Cowden disease, POEMS syndrome, Rheumatic fever, and Tuberous sclerosis-were collected from PubMed. Using the Ada-002 embedding model, disease names and symptoms were translated into vector representations in a high-dimensional space. Euclidean and Chebyshev distance metrics were used to classify symptoms based on their proximity to both the eponymic condition and the ensemble mean of the condition's symptoms.

Results: The ensemble mean approach showed significantly higher classification accuracy, correctly classifying between 80% (Cowden disease) to 100% (Tuberous sclerosis) of the sample disease symptoms using the Euclidean distance metric. In contrast, the eponymic condition approach using Euclidian distance metric and Chebyshev distances, in general, showed poor symptom classification performance, with erratic results (0%-100% accuracy), largely ranging between 0% and 3% accuracy.

Discussion: The ensemble mean captures a disease's collective symptom profile, providing a more nuanced representation than the disease name alone. However, some misclassifications were due to superficial semantic similarities, highlighting the need for LLM models trained on medical corpora.

Conclusion: The ensemble mean of symptom embeddings improves classification accuracy over the eponymic condition approach. Future efforts should focus on medical-specific training of LLMs to enhance their diagnostic accuracy and clinical utility.