# Simultaneous prediction of multiple outcomes using revised stacking algorithms

## TL;DR

This paper introduces two novel stacking algorithms that improve the simultaneous prediction of multiple drug resistances in HIV using mutation data, offering flexible multivariate modeling and outperforming existing methods.

## Contribution

The paper presents new stacking algorithms that leverage information among multiple prediction tasks, enhancing multivariate prediction accuracy in HIV drug resistance modeling.

## Key findings

- Proposed methods outperform existing multivariate prediction techniques.
- Algorithms are flexible, allowing use with any univariate prediction models.
- Cross-validation confirms improved predictive performance.

## Abstract

Motivation: HIV is difficult to treat because its virus mutates at a high rate and mutated viruses easily develop resistance to existing drugs. If the relationships between mutations and drug resistances can be determined from historical data, patients can be provided personalized treatment according to their own mutation information. The HIV Drug Resistance Database was built to investigate the relationships. Our goal is to build a model using data in this database, which simultaneously predicts the resistance of multiple drugs using mutation information from sequences of viruses for any new patient. Results: We propose two variations of a stacking algorithm which borrow information among multiple prediction tasks to improve multivariate prediction performance. The most attractive feature of our proposed methods is the flexibility with which complex multivariate prediction models can be constructed using any univariate prediction models. Using cross-validation studies, we show that our proposed methods outperform other popular multivariate prediction methods. Availability: An R package will be made available.