Towards Precision Oncology: A Predictive and Causal Lens
Name
hussain-zeshanmh-phd-eecs-2023-thesis.pdf
Description
Thesis PDF
Size
14.72 MB
Format
Adobe PDF
Checksum (MD5)
3723319e6820f9b6c707eb7f205ef15c
Author(s)
Hussain, Zeshan
Advisor(s)
Sontag, David
Date Issued
September 2023
Publisher
Massachusetts Institute of Technology
Abstract
Precision oncology promises personalized care for each patient based on a holistic view of their data. However, several methodological and translational advances are required for successful implementation of this vision in the clinic. These include building temporal models to predict a patient’s survival outcomes in response to therapy, validating these methods with experimental data from Randomized Controlled Trials (RCTs), quantifying the uncertainty in the predictions, and finally, exploring how these elements can be woven together into a clinical decision support tool. In this thesis, I explore each of these aspects in turn: i) first, I build different models of clinical time-series data, with a focus on prediction of survival outcomes and forecasting of core biomarkers, ii) next, I design methods to give additional “context” for these models, including uncertainty quantification of causal estimates and validation of these estimates using RCT data, and iii) finally, I study how these elements affect treatment decision-making via a controlled user study of a decision support tool prototype.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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