Surgical scheduling via optimization and machine learning with long-tailed data
Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2023
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| Online Access: | https://hdl.handle.net/1721.1/153163 |