APPLYING OPERATIONS RESEARCH MODELS TO PROBLEMS IN HEALTH CARE
dc.contributor.advisor | Golden, Bruce | en_US |
dc.contributor.author | Price, Stuart Patrick | en_US |
dc.contributor.department | Business and Management: Decision & Information Technologies | en_US |
dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
dc.date.accessioned | 2015-06-25T05:57:47Z | |
dc.date.available | 2015-06-25T05:57:47Z | |
dc.date.issued | 2015 | en_US |
dc.description.abstract | Intensity- modulated radiation therapy is a form of cancer treatment that directs high energy x-rays to irradiate a tumor volume. In order to minimize the damage to surround-ing tissue the radiation is delivered from multiple angles. The selection of angles is an NP-hard problem and is currently done manually in most hospitals. We use previously evaluated treatment plans to train a machine learning model to sort potential treatment plans. By sorting potential treatment plans we can find better solutions while only evalu-ating a fifth as many plans. We then construct a genetic algorithm and use our machine learning models to search the space of all potential treatment plans to suggest a potential best plan. Using the genetic algorithm we are able to find plans 2% better on average than the previously best known plans. Proton therapy is a new form of radiation therapy. We simulated a proton therapy treatment center in order to optimize patient throughput and minimize patient wait time. We are able to schedule patients reducing wait times between 20% and 35% depending on patient tardiness and absenteeism. Finally, we analyzed the impact of operations research on the treatment of pros-tate cancer. We reviewed the work that has been published in both operations research and medical journals, seeing how it has impacted policy and doctor recommendations. | en_US |
dc.identifier | https://doi.org/10.13016/M2132R | |
dc.identifier.uri | http://hdl.handle.net/1903/16565 | |
dc.language.iso | en | en_US |
dc.subject.pqcontrolled | Operations research | en_US |
dc.subject.pqcontrolled | Medical imaging and radiology | en_US |
dc.subject.pquncontrolled | IMRT | en_US |
dc.subject.pquncontrolled | Machine Learning | en_US |
dc.subject.pquncontrolled | Scheduling | en_US |
dc.title | APPLYING OPERATIONS RESEARCH MODELS TO PROBLEMS IN HEALTH CARE | en_US |
dc.type | Dissertation | en_US |
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