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

Abstract

<b>Purpose:</b> 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. <b>Materials and Methods:</b> 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 evaluated as True or False to determine the accuracy of information extraction. <b>Results:</b> Clinical texts containing patient characteristics (60 texts), medical history (50 texts), and clinical test results (25 texts) were extracted from 60 case reports. GPT models could extract information accurately with simple prompts to extract straightforward information from clinical texts. Regarding sex, GPT-4 demonstrated a significantly higher accuracy rate (95%) compared to GPT-3.5 (70%). GPT-3.5 (78%) outperformed GPT-4 (57%) in extracting body mass index (BMI). Utilizing alternative decoding strategies to sex and BMI did not practically improve the performance of the two models. In GPT-4, the revised prompts, including definitions of each sex category or the BMI formula, rectified all incorrect responses regarding sex and BMI generated during the main workflow. <b>Conclusion:</b> GPT models could perform adequately with simple prompts for extracting straightforward information. For complex tasks, incorporating task-specific definitions into the prompts is a suitable strategy than relying solely on simple prompts. Therefore, researchers and clinicians should use their expertise to create effective prompts and monitor LLM outcomes when extracting complex information from clinical texts.

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