Predicting post-surgical opioid consumption using perioperative surgical data
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2021
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| Online Access: | https://hdl.handle.net/1721.1/130199 |
| _version_ | 1811069696552730624 |
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| author | Yu, Justin,M. Eng.(Justin K.)Massachusetts Institute of Technology. |
| author2 | Peter Szolovits. |
| author_facet | Peter Szolovits. Yu, Justin,M. Eng.(Justin K.)Massachusetts Institute of Technology. |
| author_sort | Yu, Justin,M. Eng.(Justin K.)Massachusetts Institute of Technology. |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020 |
| first_indexed | 2024-09-23T08:14:28Z |
| format | Thesis |
| id | mit-1721.1/130199 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:14:28Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1301992021-03-23T03:38:19Z Predicting post-surgical opioid consumption using perioperative surgical data Yu, Justin,M. Eng.(Justin K.)Massachusetts Institute of Technology. Peter Szolovits. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. 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 2021-03-22T17:16:39Z 2021-03-22T17:16:39Z 2020 Thesis https://hdl.handle.net/1721.1/130199 1241197974 eng 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 50 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Yu, Justin,M. Eng.(Justin K.)Massachusetts Institute of Technology. Predicting post-surgical opioid consumption using perioperative surgical data |
| title | Predicting post-surgical opioid consumption using perioperative surgical data |
| title_full | Predicting post-surgical opioid consumption using perioperative surgical data |
| title_fullStr | Predicting post-surgical opioid consumption using perioperative surgical data |
| title_full_unstemmed | Predicting post-surgical opioid consumption using perioperative surgical data |
| title_short | Predicting post-surgical opioid consumption using perioperative surgical data |
| title_sort | predicting post surgical opioid consumption using perioperative surgical data |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/130199 |
| work_keys_str_mv | AT yujustinmengjustinkmassachusettsinstituteoftechnology predictingpostsurgicalopioidconsumptionusingperioperativesurgicaldata |