Fwd: [MSR Thesis Talk] Deep Survival Modeling for Personalized Prognosis and Treatment Optimization

[Artur Dubrawski]

Fyi, Mingzhu is defending next week!

AWD

---------- Forwarded message ---------
From: Mingzhu Liu <mingzhul at andrew.cmu.edu>
Date: Thu, Jul 24, 2025, 10:43 PM
Subject: [MSR Thesis Talk] Deep Survival Modeling for Personalized
Prognosis and Treatment Optimization
To: RI People <ri-people at andrew.cmu.edu>
Cc: Artur Dubrawski <awd at cs.cmu.edu>, George Chen <georgechen at cmu.edu>,
Angela Chen <angelac2 at andrew.cmu.edu>


Hello everyone,

I will be giving my MSR thesis talk on Thursday, July 31, at 10 AM EDT
in GHC 4405. Everyone is invited!

Date: Thursday, July 31, 2025
Time: 10 AM - 11:30 AM EDT
Location: GHC 4405

Title: *Deep Survival Modeling for Personalized Prognosis and Treatment
Optimization*

Abstract:
Prognostic modeling from medical data holds the promise of informing
personalized care and improving clinical decision-making. This thesis
explores two applications of survival analysis and deep learning to
estimate long-term patient risk and treatment benefit in high-impact
cardiopulmonary settings. In the first study, we develop a deep multimodal
time-to-event prediction framework that estimates patient-specific
mortality risk using chest radiographs and demographic features. Unlike
traditional binary classifiers, our approach, leveraging models such as Cox
proportional hazards and deep survival machines, accounts for
right-censoring and allows for risk estimation at arbitrary time horizons,
offering greater flexibility and clinical utility. In the second study, we
apply individualized treatment effect estimation to determine which
patients with stable ischemic heart disease are most likely to benefit from
coronary artery bypass grafting (CABG). Using a recently proposed machine
learning algorithm, Cox Mixtures with Heterogeneous Effects (CMHE), we
stratify patients based on their predicted survival gain from CABG versus
optimal medical therapy alone and validate these predictions on an external
surgical cohort. Together, these works demonstrate the potential of
survival-based machine learning models to enhance personalized risk
prediction and treatment optimization in real-world clinical scenarios.

Committee:
Prof. Artur Dubrawski (advisor)
Prof. George H. Chen
Angela Chen

Best wishes,
Mingzhu Liu
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