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 Training language models to detect acute renal failure from clinical notes resulted in similar performances when using synthetic versus authentic training data. Conclusion The use of training data derived from protected health information may not be needed.