Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes.

ABSTRACT:

Objectives

To compare performances of a classifier that leverages language models when trained on synthetic versus authentic clinical notes.

Materials and methods

A classifier using language models was developed to identify acute renal failure. Four types of training data were compared: (1) notes from MIMIC-III; and (2, 3, and 4) synthetic notes generated by ChatGPT of varied text lengths of 15 (GPT-15 sentences), 30 (GPT-30 sentences), and 45 (GPT-45 sentences) sentences, respectively. The area under the receiver operating characteristics curve (AUC) was calculated from a test set from MIMIC-III.

Results

With RoBERTa, the AUCs were 0.84, 0.80, 0.84, and 0.76 for the MIMIC-III, GPT-15, GPT-30- and GPT-45 sentences training sets, respectively.

Discussion

Trainin

SUBMITTER: Litake O 

PROVIDER: S-EPMC11105122 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature