MIMIC-III-Ext-Synthetic-Clinical-Trial-Questions

Large-language models (LLMs) show promise for extracting information from
clinical notes. Deploying these models at scale can be challenging due to high
computational costs, regulatory constraints, and privacy concerns. To address
these challenges, synthetic data distillation can be used to fine-tune
smaller, open-source LLMs that achieve performance similar to the teacher
model. These smaller models can be run on less expensive local hardware or at
a vastly reduced cost in cloud deployments.

In our recent study [1], we used Llama-3.1-70B-Instruct to generate synthetic
training examples in the form of question-answer pairs along with supporting
information. We manually reviewed 1000 of these examples and release them
here. These examples can then be used to fine-tune smaller versions of Llama
to improve their ability to extract clinical information from notes.