Generating interpretable context-aware medical concept embedding implicating expert knowledge using LLMs

Tabular data learning is crucial in medical data analysis, but it has consistently overlooked a vital component within tables: column names. We aim to leverage column names and pertinent clinical knowledge when training medical tabular data, bridging the gap between the decision-making process of medical experts and conventional models. To protect privacy, all pre-processing is done locally.

We introduce a novel algorithm, named Interpretable Context-Aware Medical Concept Embedding strategy (ICAME), designed to emulate human experts' data understanding. ICAME is among the first methods that explicitly utilize clinical knowledge in data analysis, applying LLMs for context-aware embedding generation. To ensure the patient data privacy, ICAME stores the embedding of predefined pseudo-values in the local database. This keeps private data local and minimizes the cost of online LLM API usage.

ICAME consistently improves performance across classification, regression, and prediction tasks when applied to the MIMIC-III public benchmark (average gains of 1.6%p and 2.9%p on AUROC and AUPRC, respectively). The performance improvement of ICAME is notable in a data-scarce scenario (average gains of 10.5%p and 6.3%p on AUROC and AUPRC, respectively). ICAME also provides interpretability of embeddings, confirming the incorporation of cell values and clinical knowledge.

ICAME effectively integrates explicit clinical knowledge into tabular data learning for the first time, boosting performance. Additionally, it showcases high scalability, covering numerical, categorical, and binary data types. Through extensive experiments, we demonstrate the potential of ICAME as a promising tool for future medical tabular data analysis.