# Explainable Artificial Intelligence for Pharmacovigilance: What Features Are Important When Predicting Adverse Outcomes?

**Authors:** Isaac Ronald Ward, Ling Wang, Juan lu, Mohammed Bennamoun, Girish Dwivedi, Frank M Sanfilippo  
**arXiv:** [2112.13210](https://arxiv.org/abs/2112.13210)  
**Date:** 2021-12-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2112.13210v1/

## Key points

- Explainable Artificial Intelligence (XAI) is a promising approach for understanding feature importance in Machine Learning (ML) models.
- Researchers developed an XAI-based technique for pharmacovigilance monitoring by predicting adverse outcomes using individual health information.
- The study focused on predicting the probability of Acute Coronary Syndrome (ACS) adverse outcomes based on drug history and comorbidities.
- Linked Western Australian datasets were used to extract relevant data, including drug histories, comorbidities, and other key features of individuals aged over 65 who were prescribed Musculo-skeletal system or Cardiovascular system drugs between 1993 and 2009.
- Multiple ML models were trained to predict ACS-related adverse outcomes such as death or hospitalization with a discharge diagnosis of ACS.
- ML and XAI techniques were employed to determine the contribution of specific drugs to these predictions, with SHAP slightly outperforming LIME in this context.
- Rofecoxib and celecoxib were found to have a significant positive contribution to ACS-related adverse outcome predictions on average.
- The study achieved a prediction accuracy rate of 72% for ACS-related adverse outcomes.
- ML models trained on linked administrative health datasets in conjunction with XAI algorithms can effectively quantify feature importance.
- This research lays the foundation for potential applications in pharmacovigilance monitoring techniques.

## Summary

Explainable Artificial Intelligence (XAI) has emerged as a promising approach for understanding the importance of features in Machine Learning (ML) models. In this study, researchers developed an XAI-based technique for pharmacovigilance monitoring by predicting adverse outcomes using individual health information. The study focused on predicting the probability of Acute Coronary Syndrome (ACS) adverse outcomes based on an individual's drug history and comorbidities. To achieve this, linked Western Australian datasets were utilized to extract relevant data, including drug histories, comorbidities, and other key features of individuals aged over 65 who were prescribed Musculo-skeletal system or Cardiovascular system drugs between 1993 and 2009. Multiple ML models were trained to predict ACS-related adverse outcomes such as death or hospitalization with a discharge diagnosis of ACS. To determine the contribution of specific drugs to these predictions, various ML and XAI techniques were employed. Notably, XAI libraries LIME and SHAP were used to identify important and unimportant features, with SHAP slightly outperforming LIME in this context. The analysis revealed that the drug dispensing features for rofecoxib and celecoxib had a significant positive contribution to ACS-related adverse outcome predictions on average. Moreover, the study achieved a prediction accuracy rate of 72% for ACS-related adverse outcomes. These findings demonstrate that ML models trained on linked administrative health datasets in conjunction with XAI algorithms can effectively quantify feature importance. This research lays the foundation for potential applications in pharmacovigilance monitoring techniques. With further refinement and development, these methods could contribute to improved understanding and detection of adverse outcomes associated with specific medications.