Information Extraction from Clinical Texts with Generative Pre-trained Transformer Models.

ABSTRACT: Purpose: Processing and analyzing clinical texts are challenging due to its unstructured nature. This study compared the performance of GPT (Generative Pre-trained Transformer)-3.5 and GPT-4 for extracting information from clinical text. Materials and Methods: Three types of clinical texts, containing patient characteristics, medical history, and clinical test results extracted from case reports in open-access journals were utilized as input. Simple prompts containing queries for information extraction were then applied to both models using the Greedy Approach as the decoding strategy. When GPT models underperformed in certain tasks, we applied alternative decoding strategies or incorporated prompts with task-specific definitions. The outputs generated by GPT models were eval

SUBMITTER: Kim MS 

PROVIDER: S-EPMC11866537 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature