Estimating Individualized Optimal Combination Therapies through Outcome Weighted Deep Learning Algorithms

TL;DR

This paper introduces a novel deep learning approach to estimate the best combination of treatments for individual patients, addressing the multi-label nature of therapy recommendations in complex diseases.

Contribution

It proposes an outcome weighted deep learning algorithm with Fisher consistency analysis and adaptable loss functions for personalized combination therapy estimation.

Findings

The method performs well in simulations.

Real data analysis confirms effectiveness.

Adaptive loss functions improve treatment interaction modeling.

Abstract

With the advancement in drug development, multiple treatments are available for a single disease. Patients can often benefit from taking multiple treatments simultaneously. For example, patients in Clinical Practice Research Datalink (CPRD) with chronic diseases such as type 2 diabetes can receive multiple treatments simultaneously. Therefore, it is important to estimate what combination therapy from which patients can benefit the most. However, to recommend the best treatment combination is not a single-label but a multi-label classification problem. In this paper, we propose a novel outcome weighted deep learning algorithm to estimate individualized optimal combination therapy. The fisher consistency of the proposed loss function under certain conditions is also provided. In addition, we extend our method to a family of loss functions, which allows adaptive changes based on treatment interactions. We demonstrate the performance of our methods through simulations and real data analysis.