Machine learning at the operating room of the future : a comparison of machine learning techniques applied to operating room scheduling
Name
56960392-MIT.pdf
Description
Full printable version
Size
1.97 MB
Format
Adobe PDF
Checksum (MD5)
87687765b0ec6280244fd420f0b1b15e
Author(s)
Davies, Samuel Ingraham, 1980-
Advisor(s)
Leslie Pack Kaelbling.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaves 48-50).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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