Asclepius-R : Clinical Large Language Model Built On MIMIC-III Discharge Summaries

The development of large language models tailored for handling patients'
clinical notes is often hindered by the limited accessibility and usability of
these notes due to strict privacy regulations. To address these challenges, we
first create synthetic large-scale clinical notes using publicly available
case reports extracted from biomedical literature. We then use these synthetic
notes to train our specialized clinical large language model, Asclepius. While
Asclepius is trained on synthetic data, we assess its potential performance in
real-world applications by evaluating it using real clinical notes. We
benchmark Asclepius against several other large language models, including
GPT-3.5-turbo and other open-source alternatives. To further validate our
approach using synthetic notes, we also compare Asclepius with its variants
trained on real clinical notes. Our findings convincingly demonstrate that
synthetic clinical notes can serve as viable substitutes for real ones when
constructing high-performing clinical language models. This conclusion is
supported by detailed evaluations conducted by both GPT-4 and medical
professionals. All resources--including weights, codes, and data--used in the
development of Asclepius are made publicly accessible for future research.
Specifically, this repository contains Asclepius-R, a variant of Asclepius
that was trained on MIMIC-III discharge summaries. All other resource are also
publicly accessible.