Pan Qihong, Liu Yang, Wei Shaofeng
College of Traditional Chinese Medicine, Nanchang Medical College, Nanchang, Jiangxi, China.
Organization Department of the Party Committee, Nanchang Medical College, Nanchang, Jiangxi, China.
Medicine (Baltimore). 2025 Mar 7;104(10):e41601. doi: 10.1097/MD.0000000000041601.
With the development of information and communication technology, it has become possible to improve pharmacy management system (PMS) using these technologies. Our study aims to enhance the accuracy of drug attribute classification and recommend appropriate medications to improve patient compliance and treatment outcomes through the use of a semi-supervised learning method combined with artificial intelligence (AI) technology. This study proposed a semi-supervised learning method that integrates various technologies such as PMS, electronic prescriptions, and inventory management with AI to process and analyzed drug data, which enabled dynamic inventory updates and precise drug distribution. The application of the semi-supervised learning method reduced the need for labeled data, enabled automatic identification and classification of drug attributes, and recommended suitable medications. This reduced medication errors and patient wait times, significantly enhancing the efficiency and accuracy of pharmacy drug distribution. Integrating the semi-supervised learning method and AI technology into PMS can effectively improve the accuracy of drug attribute classification and the relevance of medication recommendations. This not only helped improve patient treatment outcomes but also saved costs for hospitals and provided a feasible model for other healthcare institutions to utilize AI technology in improving drug management and patient care.
随着信息通信技术的发展，利用这些技术改进药房管理系统（PMS）已成为可能。我们的研究旨在通过使用结合人工智能（AI）技术的半监督学习方法，提高药品属性分类的准确性并推荐合适的药物，以提高患者的依从性和治疗效果。本研究提出了一种半监督学习方法，该方法将PMS、电子处方和库存管理等各种技术与AI集成，以处理和分析药品数据，从而实现动态库存更新和精确的药品分发。半监督学习方法的应用减少了对标记数据的需求，实现了药品属性的自动识别和分类，并推荐了合适的药物。这减少了用药错误和患者等待时间，显著提高了药房药品分发的效率和准确性。将半监督学习方法和AI技术集成到PMS中，可以有效提高药品属性分类的准确性和用药推荐的相关性。这不仅有助于改善患者的治疗效果，还为医院节省了成本，并为其他医疗机构利用AI技术改善药品管理和患者护理提供了一个可行的模型。
