An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions
Yükleniyor...
Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
One of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies.
Açıklama
Anahtar Kelimeler
Machine Learning, Pandemic, COVID-19, SHAP, Deep Learning, Genetic Algorithm
Kaynak
Decision Support Systems
WoS Q DeÄŸeri
Q1
Scopus Q DeÄŸeri
Q1
Cilt
161
Sayı
Künye
Davazdahemami, B., Zolbanin, H. M. ve Delen, D. (2022). An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions. Decision Support Systems, 161. https://doi.org/10.1016/j.dss.2022.113730
