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Reinforcement learning and stochastic control for sepsis treatment: the promise, obstacles and potential solutions

Nanayakkara, Thesath (2022) Reinforcement learning and stochastic control for sepsis treatment: the promise, obstacles and potential solutions. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

We develop clinically motivated, computational methods for sepsis decision-making. Sepsis is a life-threatening syndrome, with enormous mortality, morbidity, and economic burden. However, despite decades of research spanning various academic disciplines, a thorough understanding of sepsis treatment had proved elusive.

Recent advances in data-driven machine learning and control methods have led to numerous attempts to gain insight and learn intelligent treatment strategies directly from observed data. Stochastic optimal control and Reinforcement Learning, are in particular popular as they are a natural fit to formalize clinical decision-making. However, although such methods carry significant promise, there are multiple obstacles at all levels. Thus, the goal of our work is to identify, and address these challenges and propose novel solutions. In particular, we focus on formalizing the problem in a stochastic control framework, encoding physiologic domain knowledge and improving the patient state representation, and investigating associated uncertainties.

Through a combination of control theory, deep representation learning, and the integration of mechanistic modeling we introduce several improvements and novel directions to advance the current status quo of data-driven interventions for clinical sepsis. We show how our methods can supplement clinicians, provide new directions for future computational research and potentially uncover valuable hints toward better treatment strategies.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Nanayakkara, Thesathtcn10@pitt.edutcn100000-0003-0443-8475
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSwigon, Davidswigon@pitt.edu
Committee CoChairClermont, Gillescler@pitt.edu
Committee MemberLangmead, Christophercjl@cs.cmu.edu
Committee MemberTrenchea, Catalintrenchea@pitt.edu
Date: 12 October 2022
Date Type: Publication
Defense Date: 19 May 2022
Approval Date: 12 October 2022
Submission Date: 12 May 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 155
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Stochastic Control, Reinforcement Learning, Sepsis, Clinical Decision Making, AI for Medicine, Machine Learning
Date Deposited: 12 Oct 2022 15:33
Last Modified: 12 Oct 2022 15:33
URI: http://d-scholarship.pitt.edu/id/eprint/43256

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  • Reinforcement learning and stochastic control for sepsis treatment: the promise, obstacles and potential solutions. (deposited 12 Oct 2022 15:33) [Currently Displayed]

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