lesions vary from person to person, so that each person's experience with MS is unique. A doctor choosing the right treatment to mitigate MS symptoms would account for a patient's current disease state, the potential for disease worsening, a drug's side effects, and the drug's patient-specific efficacy. Making a personalized treatment decision (which may also change from visit to visit) requires an inordinate amount of work. However, an AI model that has learned outcomes for different treatments from the patient's unique MRI data across the entirety of the disease could recommend the optimal treatment instantly. In his research, Joshua combined causal machine learning estimators of treatment effect with modern probabilistic deep learning methods to improve the treatment recommendation capabilities of AI models. Using data harmonized from many clinical trials, the designed models accurately predict distributions of outcomes for all potential treatments. The probabilistic aspect of the model accounts for the natural variance in the disease, allowing clinicians to trust the model. In sum, his work demonstrates the importance of using unique patient features for treatment recommendation and the guidelines for bringing these models to the clinic. This work was funded by Mila-MSR grant in collaboration with Nick Pawlowski, MSR Cambridge. 11 DAILY CVPR Sunday Joshua Durso-Finley A patient's brain evolution over time, with lesions highlighted in red. A high-level overview of the model. A patient's unique data and lowdimensional patient descriptors are used to produce estimates for treatment outcomes and the corresponding treatment effects.
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