Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series

TL;DR

This paper presents a new method combining counterfactual estimation and uncertainty quantification to improve personalized treatment selection in clinical time series, ensuring cost-effectiveness and reliability.

Contribution

It introduces a novel approach that integrates uncertainty quantification with counterfactual estimation for optimal, cost-aware treatment recommendations in personalized medicine.

Findings

Robust performance across different counterfactual baselines

Uncertainty quantification improves treatment selection reliability

Effective in simulated cardiovascular and COVID-19 datasets

Abstract

In personalized medicine, the ability to predict and optimize treatment outcomes across various time frames is essential. Additionally, the ability to select cost-effective treatments within specific budget constraints is critical. Despite recent advancements in estimating counterfactual trajectories, a direct link to optimal treatment selection based on these estimates is missing. This paper introduces a novel method integrating counterfactual estimation techniques and uncertainty quantification to recommend personalized treatment plans adhering to predefined cost constraints. Our approach is distinctive in its handling of continuous treatment variables and its incorporation of uncertainty quantification to improve prediction reliability. We validate our method using two simulated datasets, one focused on the cardiovascular system and the other on COVID-19. Our findings indicate that our method has robust performance across different counterfactual estimation baselines, showing that introducing uncertainty quantification in these settings helps the current baselines in finding more reliable and accurate treatment selection. The robustness of our method across various settings highlights its potential for broad applicability in personalized healthcare solutions.