Advanced Search
DSpace@MIT

Machine learning at the operating room of the future : a comparison of machine learning techniques applied to operating room scheduling

Research and Teaching Output of the MIT Community

Show simple item record

dc.contributor.advisor Leslie Pack Kaelbling. en_US
dc.contributor.author Davies, Samuel Ingraham, 1980- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2005-09-26T20:08:13Z
dc.date.available 2005-09-26T20:08:13Z
dc.date.copyright 2004 en_US
dc.date.issued 2004 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/28379
dc.description Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. en_US
dc.description Includes bibliographical references (leaves 48-50). en_US
dc.description.abstract The quality of an operating room schedule is determined by the accuracy of the surgery duration estimation used. State of the art estimation algorithms consider only three surgery variables-procedure type, surgeon identity, and date of surgery-to predict the length of surgeries. We show that if we can take advantage of a richer set of available information, we can significantly improve estimation accuracy. Additional recorded (but unused) variables include patient age, gender, and morbidity, anesthesiologist identity, and surgery location. We implement and compare the accuracy of four standard machine learning algorithms that take advantage of this richer data set: linear regression, nearest neighbors, regression trees, and support vector regression. We conclude that additional variables can improve the accuracy estimate by as much as 20%. Finally, we discuss the implementation challenges and future work necessary to make machine learning techniques available to the data analyst concerned with implementation. Portions of this work are sponsored by the U.S. Dept. of the Army, under DAMD 17-02- 2-0006. The information does not necessarily reflect the position of the government, and no official endorsement should be inferred. en_US
dc.description.statementofresponsibility by Samuel Ingraham Davies. en_US
dc.format.extent 50 leaves en_US
dc.format.extent 2061626 bytes
dc.format.extent 2065592 bytes
dc.format.mimetype application/pdf
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Machine learning at the operating room of the future : a comparison of machine learning techniques applied to operating room scheduling en_US
dc.type Thesis en_US
dc.description.degree M.Eng. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 56960392 en_US


Files in this item

Name Size Format Description
56960392.pdf 1.966Mb PDF Preview, non-printable (open to all)
56960392-MIT.pdf 1.969Mb PDF Full printable version (MIT only)

This item appears in the following Collection(s)

Show simple item record

MIT-Mirage