Member-only story
Neuro-Symbolic AI and Hyperdimensional Computing for Predicting Forbidden Drug Pairs: A Comprehensive Technical Guide
Foreword
In the evolving landscape of healthcare and pharmaceuticals, ensuring patient safety through the management of drug interactions is paramount. The identification of forbidden drug pairs — combinations that can lead to adverse effects if administered together — is a critical task that can significantly enhance patient care. Leveraging advanced machine learning (ML) techniques, particularly Neuro-Symbolic AI and Hyperdimensional Computing, can revolutionize how we predict and manage these interactions.
In this comprehensive technical guide, we will walk through an extensive evaluation of three ML models — Neural Network, Random Forest, and XGBoost — applied to the task of predicting forbidden drug pairs. We will delve into the mathematical foundations of these models, dissect the underlying code, explore practical use cases, highlight the benefits, and analyze the results obtained from our experiments. Additionally, we will explore how Neuro-Symbolic AI and Hyperdimensional Computing can further enhance this predictive framework.
Introduction
Predicting forbidden drug pairs involves classifying combinations of drugs into safe (Class 1) or risky (Class 0) categories based on historical data and known interactions. This classification task is inherently imbalanced, with the majority of drug pairs being safe and a minority being risky. Addressing this imbalance while maintaining high recall for Class 1 is crucial to minimize false negatives, which could have severe implications for patient safety.
In our evaluation, we employed three ML models — Neural Network, Random Forest, and XGBoost — to tackle this classification problem. Additionally, we attempted SHAP (SHapley Additive exPlanations) analysis for model interpretability and implemented symbolic rules to enforce business logic regarding forbidden pairs. We further explored how Neuro-Symbolic AI and Hyperdimensional Computing could enhance our predictive framework.
Let’s delve into the mathematical underpinnings of these models.
