As the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.

Hospital logistics management provides critical support for clinical work, and the management of materials is key to logistics management. Based on an analysis of current logistics management of the hospital, desirable results on logistics materials management have been harvested. The measures taken include optimizing management workflow and reinforcing cost control, in combination with such efforts as regulations improvement, higher informatization level and staff teamwork building.

医院后勤物资管理是医院管理的重要组成部分。近年来，许多医疗机构规模不断扩大，而后勤物资管理仍较落后，跟不上医院的发展。对此，从促进医院后勤物资管理科学化、信息化、精细化等方面提出建议。