MED-Prompt: A novel prompt engineering framework for medicine prediction on free-text clinical notes

Abstract

Existing AI-based medicine prediction systems require substantial training time, computing resources, and extensive labeled data, yet they often lack scalability. To bridge these gaps, this study introduces a novel MED-Prompt framework that employs pretrained models such as BERT, BioBERT, and ClinicalBERT. The core of our framework lies in developing specialized prompts, which act as guiding instructions for the models during the prediction process. MED-Prompt develops prompts that help models interpret and extract medical information from clinical corpus. The clinical text was derived from the widely known MIMIC-III 1 dataset. The study performs a comparative analysis and evaluates the performance of Manual-Prompt and GPT-Prompts. Further, a fine-tuned approach is developed within MED-Prompt, leveraging transfer learning to achieve prompt-guided medicine predictions. The proposed method achieved a maximum F1-score of 96.8%, which is more than 40% F1-score higher than the pretrained model. In addition, the fine-tuned also showed an average of 2.38 times better processing performance. These results revealed that MED-Prompt is scalable regarding the number of training records and input prompts. These results not only demonstrate the proficiency and effectiveness of the framework but also significantly reduce computational requirements. This also indicates that the proposed approach has the potential to significantly improve patient care, reduce resource requirements, and increase the overall effectiveness of AI-driven medical prediction systems.

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