Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/121646 |
| _version_ | 1811076245080768512 |
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| author | Zhou, Helen(Helen L.) |
| author2 | David Sontag and Sanjat Kanjilal. |
| author_facet | David Sontag and Sanjat Kanjilal. Zhou, Helen(Helen L.) |
| author_sort | Zhou, Helen(Helen L.) |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T10:18:35Z |
| format | Thesis |
| id | mit-1721.1/121646 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:18:35Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1216462019-08-07T03:04:35Z Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship Zhou, Helen(Helen L.) David Sontag and Sanjat Kanjilal. 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. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 93-96). Antibiotics are critical to modern medicine. However, levels of resistance have been rising, exacerbated by over-prescription and misuse of antibiotics. One major reason for this inappropriate usage is that doctors often must decide treatment without the results of microbiologic testing, a setting known as the empiric treatment setting. Thus, this work aims to provide clinical decision support through patient-specific predictions of resistance at the point of care. Combining information from diagnoses, procedures, medications, clinicians' notes, and other modalities present in electronic medical records, various machine learning models such as logistic regression and decision trees are used to predict patients' probabilities of resistance to various antibiotics. The full dataset consists of electronic medical records from patients presenting to the Massachusetts General Hospital and the Brigham & Women's Hospital between 2007 and 2016. On samples from the urinary tract (UTIs), which comprise approximately 48% of microbiology samples, the models achieve test AUCs ranging from 0.665 to 0.955 (depending on the antibiotic). To evaluate the practical utility of these models, we extract the uncomplicated UTI cohort. Combining model predictions with well-defined treatment guidelines, a decision algorithm is constructed to recommend antibiotic treatments. For uncomplicated UTIs, the algorithm reduces test set prescriptions of broad-spectrum antibiotics by about 6.6%, while retaining similar levels of inappropriate antibiotic therapy. by Helen Zhou. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-07-15T20:30:41Z 2019-07-15T20:30:41Z 2018 2018 Thesis https://hdl.handle.net/1721.1/121646 1098216500 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 96 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Zhou, Helen(Helen L.) Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship |
| title | Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship |
| title_full | Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship |
| title_fullStr | Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship |
| title_full_unstemmed | Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship |
| title_short | Large-scale prediction of patient-level antibiotic resistance : towards clinical decision support for improved antimicrobial stewardship |
| title_sort | large scale prediction of patient level antibiotic resistance towards clinical decision support for improved antimicrobial stewardship |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/121646 |
| work_keys_str_mv | AT zhouhelenhelenl largescalepredictionofpatientlevelantibioticresistancetowardsclinicaldecisionsupportforimprovedantimicrobialstewardship |