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Fig. 1 | BMC Medical Informatics and Decision Making

Fig. 1

From: Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages

Fig. 1

Data Processing and Model Performance for RTI Identification: This figure shows the filtering of URTI symptoms from the dataset after processing all cases. It focuses on the analysis of 5,350 poorly labeled cases, comparing the ROC-AUC performance of the pretrained and fine-tuned GPT-3 models. The fine-tuned model demonstrates significant improvement in identifying RTI cases

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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