TransAMR: An Interpretable Transformer Model for Accurate Prediction of Antimicrobial Resistance Using Antibiotic Administration Data

Abstract

Antimicrobial Resistance (AMR) is a growing public and veterinary health concern, and the ability to accurately predict AMR from antibiotics administration data is crucial for effectively treating and managing infections. While genomics-based approaches can provide better results, sequencing, assembling, and applying Machine Learning (ML) methods can take several hours. Therefore, alternative approaches are required. This study focused on using ML for antimicrobial stewardship by utilising data extracted from hospital electronic health records, which can be done in real-time, and developing an interpretable 1D-Transformer model for predicting AMR. A multi-baseline Integrated Gradient pipeline was also incorporated to interpret the model, and quantitative validation metrics were introduced to validate the model. The performance of the proposed 1D-Transformer model was evaluated using a dataset of urinary tract infection (UTI) patients with four antibiotics. The proposed 1D-Transformer model achieved 10% higher area under curve (AUC) in predicting AMR and outperformed traditional ML models. The Explainable Artificial Intelligence (XAI) pipeline also provided interpretable results, identifying the signatures contributing to the predictions. This could be used as a decision support tool for personalised treatment, introducing AMR-aware food and management of AMR, and it could also be used to identify signatures for targeted interventions.

Keywords

• Transformer
• multi-drug AMR
• antimicrobial stewardship
• missing labels
• XAI
• multi-label prediction