{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/144928"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/144928","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Evaluating Learned and Rule-Based Policies for Hospital Bed Assignment","abstract":"In many complex sequential decision making problems in healthcare such as hospital bed assignment, resources are limited and shared between patients. Hospital bed assignment is an important decision making problem because a patient's bed assignment influences their medical outcomes, including their risk of developing a healthcare associated infection (HAI). In this thesis, we consider the problem of assigning patients to hospital beds with the goal of reducing the incidence of HAIs. We propose a two part approach to this task: first, use reinforcement learning to learn a function from logged data for assessing different patient and bed pairs, then use this function to design policies for sequentially assigning batches of patients to beds. We develop a simulation to demonstrate this approach and conduct experiments exploring how assumptions about the environment affect the performance of learned and rule-based policies. We examine the performance of weighted importance sampling for off-policy evaluation. Our results show that policies that prioritize patients with the highest risk of poor outcomes outperform purely greedy policies.","abstract_html":"In many complex sequential decision making problems in healthcare such as hospital bed assignment, resources are limited and shared between patients. Hospital bed assignment is an important decision making problem because a patient's bed assignment influences their medical outcomes, including their risk of developing a healthcare associated infection (HAI). In this thesis, we consider the problem of assigning patients to hospital beds with the goal of reducing the incidence of HAIs. We propose a two part approach to this task: first, use reinforcement learning to learn a function from logged data for assessing different patient and bed pairs, then use this function to design policies for sequentially assigning batches of patients to beds. We develop a simulation to demonstrate this approach and conduct experiments exploring how assumptions about the environment affect the performance of learned and rule-based policies. We examine the performance of weighted importance sampling for off-policy evaluation. Our results show that policies that prioritize patients with the highest risk of poor outcomes outperform purely greedy policies.","abstract_has_math":false,"creators":["Wong, Hallee E."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Hospital bed assignment is an important decision making problem because a patient's bed assignment influences their medical outcomes, including their risk of developing a healthcare associated infection (HAI). In this thesis, we consider the problem of assigning patients to hospital beds with the goal of reducing the incidence of HAIs. We propose a two part approach to this task: first, use reinforcement learning to learn a function from logged data for assessing different patient and bed pairs, then use this function to design policies for sequentially assigning batches of patients to beds. We develop a simulation to demonstrate this approach and conduct experiments exploring how assumptions about the environment affect the performance of learned and rule-based policies. We examine the performance of weighted importance sampling for off-policy evaluation. Our results show that policies that prioritize patients with the highest risk of poor outcomes outperform purely greedy policies."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Evaluating Learned and Rule-Based Policies for Hospital Bed Assignment"]}]}],"canonical_facts":{"dc:contributor.advisor":["Guttag, John V."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Wong, Hallee E."],"dc:date.accessioned":["2022-08-29T16:21:34Z"],"dc:date.available":["2022-08-29T16:21:34Z"],"dc:date.issued":["2022-05"],"dc:description.abstract":["In many complex sequential decision making problems in healthcare such as hospital bed assignment, resources are limited and shared between patients. Hospital bed assignment is an important decision making problem because a patient's bed assignment influences their medical outcomes, including their risk of developing a healthcare associated infection (HAI). In this thesis, we consider the problem of assigning patients to hospital beds with the goal of reducing the incidence of HAIs. We propose a two part approach to this task: first, use reinforcement learning to learn a function from logged data for assessing different patient and bed pairs, then use this function to design policies for sequentially assigning batches of patients to beds. We develop a simulation to demonstrate this approach and conduct experiments exploring how assumptions about the environment affect the performance of learned and rule-based policies. We examine the performance of weighted importance sampling for off-policy evaluation. Our results show that policies that prioritize patients with the highest risk of poor outcomes outperform purely greedy policies."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/144928"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Evaluating Learned and Rule-Based Policies for Hospital Bed Assignment"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:47Z"}