Predicting post-surgical opioid consumption using perioperative surgical data
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1241197974-MIT.pdf
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453.48 KB
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Author(s)
Yu, Justin,M. Eng.(Justin K.)Massachusetts Institute of Technology.
Advisor(s)
Peter Szolovits.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Improper consumption of prescription opioids is a massive public health issue in the United States currently. Here, we propose one approach of tackling this issue through using machine learning techniques to predict opioid consumption post discharge for surgical patients. Through the data collected from surgical patients at BIDMC, relevant features will be identified and used to predict if patients high, outlier consumption. Using logistic regression and gradient boosted decision trees, model performance were evaluated at AUCs of 0.7270 and 0.7289 respectively.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
"May 2020." Date of graduation confirmed by MIT Registrar Office. Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 49-50).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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