How Many Costly Simulations Do we Need to Create Accurate Metamodels? A Case Study on Predicting HIV Viral Load in Response to Clinically Relevant Intervention Scenarios

Abstract

Computer simulations are used in precision medicine to assist in adapting treatment plans for varying patient characteristics, especially for diseases like HIV where these characteristics have a major impact on disease trajectory. However, simulations are computationally intensive, which can be prohibitive at scale. Meta-models for HIV progression have been developed previously to approximate these simulation results more efficiently, but we are interested in determining how much data is required to build an accurate meta-model. Using many different amounts of data from two HIV simulation models, we build machine learning classification meta-models to predict if an HIV patient is at risk for AIDS based on treatment parameters. Our findings indicate that the amount required to achieve high meta-model accuracy varies for different computer simulations. We are able to achieve near-perfect accuracy with one of our models using limited data, while the other model requires more extensive data to achieve stable accuracy.