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A Modified Q-Learning Approach for Predicting Mortality in Patients Diagnosed with Sepsis

Dunn, Noah M
http://orcid.org/0000-0002-9419-7953
http://rave.ohiolink.edu/etdc/view?acc_num=miami1618439089761747

Abstract Details

2021, Master of Computer Science, Miami University, Computer Science and Software Engineering.
Among the medical crises of modern times, medically-diagnosed Sepsis persists as an ongoing condition yielding high mortality across all spectra of patients. Patients with Sepsis suffer variable symptoms, making it hard to evaluate severity, and the outcome of patients who develop Sepsis can range from full-recovery to death. The FOOTON model proposed in this study (named for its use of the qSOFA and SOFA scoring metrics) seeks to aid physicians in evaluating patient condition severity. The FOOTON model makes use of a stratified, cross-validated version of data provided by the MIMIC-III dataset to construct four variants of a binary classification model. Upon the completion of the model construction, the models can take patient data as input and they will output their prediction on the binary outcome of the patient (life or death). Of the four models produced, two models were shown to have the most promise. The imbalanced, unweighted variant of the model which performs at an average accuracy of 78.413% overall, and a balanced weighted variant, which performs at an average accuracy of 61.638% for dead patients. Providing this capability as an assistive tool for physicians can allow for the prioritization of limited resources to individuals at a greater risk of dying, with the potential to decrease overall patient mortality.
Dhananjai Rao (Advisor)
Philippe Giabbanelli (Committee Member)
Vaskar Raychoudhury (Committee Member)
69 p.
Computer Science
Sepsis, Machine Learning, Q-Learning, MDP, ICU, MIMIC-III, Mortality, In-Hospital, Noah Dunn, Model, Reinforcement Learning

Recommended Citations

Citations

  • Dunn, N. M. (2021). A Modified Q-Learning Approach for Predicting Mortality in Patients Diagnosed with Sepsis [Master's thesis, Miami University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=miami1618439089761747

    APA Style (7th edition)

  • Dunn, Noah. A Modified Q-Learning Approach for Predicting Mortality in Patients Diagnosed with Sepsis. 2021. Miami University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=miami1618439089761747.

    MLA Style (8th edition)

  • Dunn, Noah. "A Modified Q-Learning Approach for Predicting Mortality in Patients Diagnosed with Sepsis." Master's thesis, Miami University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1618439089761747

    Chicago Manual of Style (17th edition)

miami1618439089761747
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© 2021, some rights reserved.Creative Commons License A Modified Q-Learning Approach for Predicting Mortality in Patients Diagnosed with Sepsis by Noah M Dunn is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Miami University and OhioLINK.