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Title:
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EVALUATION OF COST-SAVING MACHINE LEARNING
METHODS FOR PATIENT BLOOD MANAGEMENT |
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Author(s):
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Davide Brinati, Andrea Seveso, Paolo Perazzo, Giuseppe Banfi and Federico Cabitza |
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ISBN:
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978-989-8704-18-4 |
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Editors:
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Mário Macedo |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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Patient Blood Management, Machine Learning, Sensitivity Analysis, Operation Costs |
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Type:
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Short |
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First Page:
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183 |
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Last Page:
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189 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Our objective is the development of cost-saving methods for the patient blood management in Galeazzi Orthopedic
Institute, a large Italian hospital. The methods have been developed in relation to the known costs of the hospital, both in
terms of unused blood bags and drugs. Observational data about 4593 patients have been retrieved, with anagraphical and
pre-operational clinical features. Model's performances have been compared to an existing baseline in terms of both
accuracy measures (F1, recall, AUC) and saved costs per patient. The proposed methods recorded an enhancement of
performances for the adopted measures, demonstrating a possible useful application of machine-learning-based methods
for the patient blood management task. |
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