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药物提取与药物相互作用聊天机器人:基于生成式预训练变换器的药物-药物相互作用聊天机器人

Medication Extraction and Drug Interaction Chatbot: Generative Pretrained Transformer-Powered Chatbot for Drug-Drug Interaction.

作者信息

Kim Won Tae, Shin Jaegwang, Yoo In-Sang, Lee Jae-Woo, Jeon Hyun Jeong, Yoo Hyo-Sun, Kim Yongwhan, Jo Jeong-Min, Hwang ShinJi, Lee Woo-Jeong, Park Seung, Kim Yong-June

机构信息

Department of Urology, Chungbuk National University Hospital, Cheongju, Republic of Korea.

Department of Urology, College of Medicine, Chungbuk National University, Cheongju, Republic of Korea.

出版信息

Mayo Clin Proc Digit Health. 2024 Oct 9;2(4):611-619. doi: 10.1016/j.mcpdig.2024.09.001. eCollection 2024 Dec.

Abstract

OBJECTIVE

To assist individuals, particularly cancer patients or those with complex comorbidities, in quickly identifying potentially contraindicated medications when taking multiple drugs simultaneously.

PATIENTS AND METHODS

In this study, we introduce the Medication Extraction and Drug Interaction Chatbot (MEDIC), an artificial intelligence system that integrates optical character recognition and Chat generative pretrained transformer through the Langchain framework. Medication Extraction and Drug Interaction Chatbot starts by receiving 2 drug bag images from the patient. It uses optical character recognition and text similarity techniques to extract drug names from the images. The extracted drug names are then processed through Chat generative pretrained transformer and Langchain to provide the user with information about drug contraindications. The MEDIC responds to the user with clear and concise sentences to ensure the information is easily understandable. This research was conducted from July 1, 2022 to April 30, 2024.

RESULTS

This streamlined process enhances the accuracy of drug-drug interaction detection, providing a crucial tool for health care professionals and patients to improve medication safety. The proposed system was validated through rigorous evaluation using real-world data, reporting high accuracy in drug-drug interaction identification and highlighting its potential to benefit medication management practices considerably.

CONCLUSION

By implementing MEDIC, contraindicated medications can be identified using only medication packaging, and users can be alerted to potential drug adverse effects, thereby contributing to advancements in patient care in clinical settings.

摘要

目的

帮助个人,尤其是癌症患者或患有复杂合并症的患者,在同时服用多种药物时快速识别潜在的禁忌药物。

患者与方法

在本研究中,我们引入了药物提取与药物相互作用聊天机器人(MEDIC),这是一个通过Langchain框架集成了光学字符识别和Chat生成式预训练变换器的人工智能系统。药物提取与药物相互作用聊天机器人首先接收患者的2张药袋图像。它使用光学字符识别和文本相似性技术从图像中提取药物名称。然后,将提取的药物名称通过Chat生成式预训练变换器和Langchain进行处理,为用户提供有关药物禁忌的信息。MEDIC用清晰简洁的句子回复用户,以确保信息易于理解。本研究于2022年7月1日至2024年4月30日进行。

结果

这种简化的流程提高了药物相互作用检测的准确性,为医疗保健专业人员和患者提供了一个关键工具,以提高用药安全性。所提出的系统通过使用真实世界数据进行严格评估得到验证,报告显示在药物相互作用识别方面具有很高的准确性,并突出了其对药物管理实践有显著益处的潜力。

结论

通过实施MEDIC,仅使用药物包装就能识别禁忌药物,并可提醒用户注意潜在的药物不良反应,从而有助于临床环境中患者护理的进步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e68b/11975985/1cdc5110ae8f/gr1.jpg

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