An explanatory analytics framework for early detection of chronic risk factors in pandemics
Yükleniyor...
Dosyalar
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors, use them to predict patients at high risk of mortality, and analyze the risk factors at the individual level for each high-risk patient. The proposed framework was validated using data from a repository of electronic health records of early COVID-19 patients in the US. A chronological analysis of the chronic risk factors identified using our proposed approach revealed that those factors could have been identified months before they were determined by clinical studies and/or announced by the United States health officials.
Açıklama
Anahtar Kelimeler
Machine Learning, Explanatory Artificial Intelligence, Pandemic Risk Analysis Pandemic, Diagnostic Analytics
Kaynak
Healthcare Analytics
WoS Q Değeri
Scopus Q Değeri
Q3
Cilt
2
Sayı
Künye
Davazdahemami, B., Zolbanin, H. M. ve Delen, D. (2022). An explanatory analytics framework for early detection of chronic risk factors in pandemics. Healthcare Analytics, 2, 100020. https://doi.org/10.1016/j.health.2022.100020
