Authors:
Majed Hadid
;
Adel Elomri
and
Regina Padmanabhan
Affiliation:
Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
Keyword(s):
Cancer Care, Data Analytics, Machine Learning, Decision Support.
Abstract:
The rise in demand for cancer care services, particularly outpatient chemotherapy, highlights the importance of improving the management of outpatient chemotherapy operations (OCOM). Despite the numerous studies addressing OCOM issues, the existing literature has mostly focused on problem-driven research. In this study, we aimed to utilize data-driven research to identify opportunities for improvement and address research challenges. To achieve this goal, we collected extensive operational data from a large chemotherapy center and performed a thorough analysis. Our findings revealed four key research challenges, including the prediction of length of stay, change in patient drug posting weight, delay in appointment admission, and stochasticity in drug administration duration. To address these challenges, we developed two machine learning models to predict these outcomes, utilizing 15 features and highlighting the most important features. Our results showed an efficient performance in
predicting the outcomes using the XGBoost model, emphasizing the potential of data-driven research in improving OCOM.
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