MAPRS: An intelligent approach for post-prescription review based on multi-label learning.

Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way to improve appropriate prescription by the review and stewardship of antimicrobials, and Post-Prescription Review (PPR) is currently the main tool used in hospitals. Existing methods of PPR typically focus on the dichotomy of antimicrobial prescription based on binary classification which, however, is usually a multi-label classification problem. Moreover, previous research did not explain the causes beneath the inappropriate antimicrobial used in the clinical setting, which could be practically important for problem location and decision improvement. In this paper, we collected antimicrobial prescriptions and related data from clean surgery in a hospital in northeastern China, and proposed a Multi-label Antimicrobial Post-Prescription Review System (MAPRS). MAPRS first uses NLP techniques to process unstructured data in prescriptions and explores the value of clinical record text for solving medical problems. Then, Classifier Chains are used to deal with multi-label problems and fused with machine learning algorithms to construct a classifier. At last, a SHAP explanation module is introduced to explain the inappropriate prescriptions. The experimental results show that MAPRS could achieve great performance in a challenging six-category multi-label task, with a subset accuracy of 90.7 % and an average AUROC of 94.3 %. Our results can help hospitals to perform intelligent prescription review and improve the antimicrobial stewardship.