Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
This addresses the challenge of identifying harmful drug interactions for older populations, but it is incremental as it builds on existing neural bandit methods for a specific domain.
The paper tackled the problem of efficiently searching for dangerous drug combinations (polypharmacies) in large claims databases, proposing an optimization strategy that detected up to 72% of potentially inappropriate polypharmacies with 99% average precision in simulations.
Polypharmacy, most often defined as the simultaneous consumption of five or more drugs at once, is a prevalent phenomenon in the older population. Some of these polypharmacies, deemed inappropriate, may be associated with adverse health outcomes such as death or hospitalization. Considering the combinatorial nature of the problem as well as the size of claims database and the cost to compute an exact association measure for a given drug combination, it is impossible to investigate every possible combination of drugs. Therefore, we propose to optimize the search for potentially inappropriate polypharmacies (PIPs). To this end, we propose the OptimNeuralTS strategy, based on Neural Thompson Sampling and differential evolution, to efficiently mine claims datasets and build a predictive model of the association between drug combinations and health outcomes. We benchmark our method using two datasets generated by an internally developed simulator of polypharmacy data containing 500 drugs and 100 000 distinct combinations. Empirically, our method can detect up to 72% of PIPs while maintaining an average precision score of 99% using 30 000 time steps.