CPLLM: Clinical prediction with large language models.

We present Clinical Prediction with Large Language Models (CPLLM), a method that involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical disease and readmission. We utilized quantization and fine-tuned the LLM using prompts. For diagnostic predictions, we predicted whether patients would be diagnosed with a target disease during their next visit or in the subsequent diagnosis, leveraging their historical medical records.

Federated learning of medical concepts embedding using BEHRT.

Objectives: Electronic health record data is often considered sensitive medical information. Therefore, the EHR data from different medical centers often cannot be shared, making it difficult to create prediction models using multicenter EHR data, which is essential for such models' robustness and generalizability. Federated learning (FL) is an algorithmic approach that allows learning a shared model using data in multiple locations without the need to store all data in a single central place.

MedConceptsQA: Open source medical concepts QA benchmark.

Background: Clinical data often includes both standardized medical codes and natural language texts. This highlights the need for Clinical Large Language Models to understand these codes and their differences. We introduce a benchmark for evaluating the understanding of medical codes by various Large Language Models.