A machine learning automation system for utilization management
Author(s)
Verma, Rohil.
Download1227276857-MIT.pdf (4.471Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Robert M. Freund.
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Metadata
Show full item recordAbstract
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).
Date issued
2020Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.