mlsanityData to 14 Oct 2026

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models

arXiv 2305.03829 ↗First posted 5 May 2023cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

9 of 28 references.

References · 9

  1. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 478
  3. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  4. 2018Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentationcited by 146
  5. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  6. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  7. 2017Uplift Modeling with Multiple Treatments and General Response Typescited by 41
  8. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  9. 2022Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRIcited by 7

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Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 14
  2. 2024Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEscited by 1
  3. 2025UNSURF: Uncertainty Quantification for Cortical Surface Reconstruction of Clinical Brain MRIscited by 1
  4. 2024Enhancing predictive imaging biomarker discovery through treatment effect analysiscited by 0
  5. 2025Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatmentscited by 0