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Unifying Data-Driven Modeling with Machine Learning to Improve Personalized Treatment of Critical Care Patients

McLaverty, Brian (2023) Unifying Data-Driven Modeling with Machine Learning to Improve Personalized Treatment of Critical Care Patients. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Sepsis is a life-threatening organ dysfunction resulting from a dysregulated host response to infection. Early identification and appropriate management of sepsis has been identified as a global health priority. The development and validation of patient-specific models of acute inflammation offer the opportunity to expand current knowledge of underlying interpatient differences in the fight against infection. Early prediction into identified sepsis subtypes exhibiting differing health outcomes could be used as a guide for clinicians to develop a personalized therapeutic regimen.

A dynamical model of inflammation response in sepsis was derived, leveraging cytokine mediators that play a key role in sepsis pathology. The model was validated against a selected cohort of sepsis patients from the Protocolized Care for Early Septic Shock (ProCESS) trial to create patient-specific models of inflammation. Using the fitted model parameters and unsupervised machine learning, four subtypes of septic patients emerged with distinct inflammation responses and clinical outcomes. A clinical tool was proposed that accurately predicts patient membership into subtypes exhibiting high or low mortality risk using cytokine levels measured within six hours of hospital admission.

Dialysis patients frequently experience intradialytic hypotension (IDH), which is an independent predictor of mortality. Development of a personalized treatment support system for hemodialysis that reduces risk of IDH could potentially improve dialysis patient outcomes. A predictive model of future risk of IDH was developed by training and testing a random forest model using electronic health record data from hemodialysis treatments performed at UPMC acute care facilities. The model forecasted future IDH several hours ahead of occurrence and produced dynamic risk evolution toward instability. An early-warning tool was developed that accurately classifies a patient as high or low risk for IDH using model-derived risk scores available within minutes of dialysis initiation. A risk-based reinforcement learning algorithm was then developed that recommends preemptive, personalized treatment. The agent-suggested treatment strategy resulted in decreased incidence of hypotension and increased accomplishment of individualized fluid goals in silico at the cost of increased intervention. Clinicians could use the tools presented in this work to provide timely, individualized treatment to patients and potentially improve sepsis and hemodialysis patient outcomes.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
McLaverty, Brianbrian.mclaverty@pitt.edubpm44
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairParker, Robert S.rparker@pitt.edurparker
Committee CoChairClermont, Gillescler@pitt.educler
Committee MemberShoemaker, Jasonjason.shoemaker@pitt.edujas518
Committee MemberLee, Hyo Kyunghyo.lee@pitt.eduhkl14
Date: 13 June 2023
Date Type: Publication
Defense Date: 19 January 2023
Approval Date: 13 June 2023
Submission Date: 25 March 2023
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 119
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical and Petroleum Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Dynamic Modeling, Reinforcement Learning, Machine Learning, Risk Modeling, Hemodialysis, Sepsis
Date Deposited: 13 Jun 2023 14:07
Last Modified: 13 Jun 2023 14:07


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