A machine learning automation system for utilization management
Name
1227276857-MIT.pdf
Size
4.47 MB
Format
Adobe PDF
Checksum (MD5)
aacfc7fc81517c53cbc0de5b48940075
Author(s)
Verma, Rohil.
Advisor(s)
Robert M. Freund.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
We develop high-performance machine learning automation systems for utilization management that are effective across all specialties. We were motivated by the knowledge that current automation systems for utilization management are rules- based and focused on narrow subsets of healthcare specialties. We develop models that can automate nearly 90% of a utilization management team's workload with less than 1% error. We evaluate these models on both historical data and as part of a live system in industry. The performance and efficacy of our models are consistent across both evaluation domains, demonstrating the generalizability of our work.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 87-90).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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.
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