Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved inform...Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.展开更多
文摘Background:Acquiring relevant information about procurement targets is fundamental to procuring medical devices.Although traditional Natural Language Processing(NLP)and Machine Learning(ML)methods have improved information retrieval efficiency to a certain extent,they exhibit significant limitations in adaptability and accuracy when dealing with procurement documents characterized by diverse formats and a high degree of unstructured content.The emergence of Large Language Models(LLMs)offers new possibilities for efficient procurement information processing and extraction.Methods:This study collected procurement transaction documents from public procurement websites,and proposed a procurement Information Extraction(IE)method based on LLMs.Unlike traditional approaches,this study systematically explores the applicability of LLMs in both structured and unstructured entities in procurement documents,addressing the challenges posed by format variability and content complexity.Furthermore,an optimized prompt framework tailored for procurement document extraction tasks is developed to enhance the accuracy and robustness of IE.The aim is to process and extract key information from medical device procurement quickly and accurately,meeting stakeholders'demands for precision and timeliness in information retrieval.Results:Experimental results demonstrate that,compared to traditional methods,the proposed approach achieves an F1 Score of 0.9698,representing a 4.85%improvement over the best baseline model.Moreover,both recall and precision rates are close to 97%,significantly outperforming other models and exhibiting exceptional overall recognition capabilities.Notably,further analysis reveals that the proposed method consistently maintains high performance across both structured and unstructured entities in procurement documents while balancing recall and precision effectively,demonstrating its adaptability in handling varying document formats.The results of ablation experiments validate the effectiveness of the proposed prompting strategy.Conclusion:Additionally,this study explores the challenges and potential improvements of the proposed method in IE tasks and provides insights into its feasibility for real-world deployment and application directions,further clarifying its adaptability and value.This method not only exhibits significant advantages in medical device procurement but also holds promise for providing new approaches to information processing and decision support in various domains.
