2026-09-20T11:54:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1301992026-06-06T00:48:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Peter Szolovits.
Yu, Justin,M. Eng.(Justin K.)Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-03-22T17:16:39Z
2021-03-22T17:16:39Z
2020
https://hdl.handle.net/1721.1/130199
1241197974
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).
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.
by Justin Yu.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
50 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Predicting post-surgical opioid consumption using perioperative surgical data
Thesis
application/pdf
2021-03-22T17:16:07Z
Publication
Master
EECS
unknown
unknown
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<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.</Abstract>
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