Clinical Prognosis Evaluation of Covid-19 Patients: an Interpretable Hybrid Machine Learning Approach
Yükleniyor...
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
Özet
This retrospective cohort study deals with evaluating severity of COVID-19 cases on the first symptoms and blood-test results of infected patients admitted to Emergency Department of Koc University Hospital (Istanbul, Turkey). To figure out remarkable hematological characteristics and risk factors in the prognosis evaluation of COVID-19 cases, the hybrid machine learning (ML) approaches integrated with feature selection procedure based Genetic Algorithms and information complexity were used in addition to the multivariate statistical analysis. Specifically, COVID-19 dataset includes demographic features, symptoms, blood test results and disease histories of total 166 inpatients with different age and gender groups. Analysis results point out that the hybrid ML methods has brought out potential risk factors on the severity of COVID-19 cases and their impacts on the prognosis evaluation, accurately.
Açıklama
Anahtar Kelimeler
Artificial intelligence, COVID-19, Clinical prognosis, Feature selection, ICOMP, Machine learning, Severity of COVID-19
Kaynak
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Current Research in Translational Medicine 70 (2022) 103319
