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Research Objectives:
Advances in technology have changed the face of global healthcare through utilizing AI,
deep learning, machine learning, IoT, big data, and prescriptive analytics. These changes can be seen as appearance
of better monitoring or diagnostic tools, or solutions for better managements of limited resources.
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Under Review:
Lanier, D. and Masoud, S., A Predictive Framework for Patients’ Otolaryngology Outpatient Visit Scope Need,
Special Issue of IISE Transactions on Health Systems: Innovation of Digital Healthcare Solutions,
Under Review.
Peer-reviewed publications:
Emakhu, J., Monplaisir, L., Aguwa, C., Arslanturk, S., Masoud, S., Nassereddine, H., ..., Miller, J. B. (2022). Acute Coronary Syndrome Prediction in Emergency Care: A Machine Learning Approach.
Computer Methods and Programs in Biomedicine,
107080.
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Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Esfahanian, F., Masoud, S. (2022). Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes.
Medical and Biological Engineering and Computing,
60(8), 2423-2444.
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Etu, E. E., Monplaisir, L., Masoud, S., Arslanturk, S., Emakhu, J., Tenebe, I., ... , Krupp, S. (2022, June). A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic.
Healthcare,
Vol. 10, No. 6, p. 1120, MDPI.
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Etu, E. E., Monplaisir, L., Aguwa, C., Arslanturk, S., Masoud, S., Markevych, I., Miller, J. (2022). Identifying indicators influencing emergency department performance during a medical surge: A consensus-based modified fuzzy Delphi approach.
PloS one,
17(4), e0265101.
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Etu, E. E., Monplaisir, L., Arslanturk, S., Masoud, S., Aguwa, C., Markevych, I., Miller, J. (2022). Prediction of Length of Stay in the Emergency Department for COVID-19 Patients: A Machine Learning Approach.
IEEE Access,
10, 42243-42251.
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Emakhu, J., Monplaisir, L., Aguwa, C., Arslanturk, S., Masoud, S., Hamam, M., ... , Miller, J. (2021). An Ensemble Learning Approach to Predict Non-ST-Segment Elevation Myocardial Infarction in Patients With Clinical Concerns for Acute Coronary Syndrome.
Annals of Emergency Medicine,
78(4), S81-S82.
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Etu, E. E., Monplaisir, L., Aguwa, C., Arslanturk, S., Masoud, S., Krupp, S., ..., Miller, J. (2021). 33 Forecasting Daily Patient Arrivals during COVID-19 in Emergency Departments: A Deep Learning Approach.
Annals of Emergency Medicine,
78(4), S14.
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Etu, E. E., Monplaisir, L., Aguwa, C., Masoud, S., Arslanturk, S., Markevych, I., Miller, J. (2020, January). A Comparison of Traditional and Modified Fuzzy Delphi in Identification of Metrics for ED Performance Associated with Medical Surges. In Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management Detroit, Michigan, USA2020.
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