Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.
Code (0)
등록된 구현이 없습니다.
Tasks
counterfactualDecision MakingSimilar Papers 제목 키워드 기반
Instrumental and Proximal Causal Inference with Gaussian Processes
Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial methodological advances, existing approac…
Gaussian ProcessesCausal InferenceA Structural Causal Model for MR Images of Multiple Sclerosis
Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?" These types of questions are causal in nature and require the tools of causal infe…
Causal InferencecounterfactualCounterfactual InferenceDisease PredictionUncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cance…
Uncertainty QuantificationCausal Machine Learning Methods for Estimating Personalised Treatment Effects -- Insights on validity from two large trials
Causal machine learning (ML) methods hold great promise for advancing precision medicine by estimating personalized treatment effects. However, their reliability remains largely unvalidated in empirical settings. In this…
Uncertainty-Aware Spatial Color Correlation for Low-Light Image Enhancement
Most existing low-light image enhancement approaches primarily focus on architectural innovations, while often overlooking the intrinsic uncertainty within feature representations particularly under extremely dark condit…
Low-Light Image Enhancement