An artificial neural network classification method employing longitudinally monitored immune biomarkers to predict the clinical outcome of critically ill COVID-19 patients

PeerJ

article

Gustavo Martinez¹  Alexis Garduno³  Abdullah Mahmud-Al-Rafat²  Ali Toloue Ostadgavahi²  Ann Avery⁴  Scheila de Avila e Silva⁵  Rachael Cusack³  Cheryl Cameron⁶  Mark Cameron⁷  Ignacio Martin-Loeches³  David Kelvin¹ 

[1] Immunology, Shantou University;Microbiology and Immunology, Dalhousie University;Department of Clinical Medicine, University of Dublin, Trinity College;Division of Infectious Diseases, MetroHealth Medical Center;Department of Biotechnology, Universidade de Caxias do Sul;Department of Nutrition, Case Western Reserve University;Department of Population & Quantitative Health Sciences, Case Western Reserve University

关键词: Biomarkers;    Artificial Neural Networks;    Classification;    Immunology;    Deep learning;   

DOI  :  10.7717/peerj.14487

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

BackgroundThe severe form of COVID-19 can cause a dysregulated host immune syndrome that might lead patients to death. To understand the underlying immune mechanisms that contribute to COVID-19 disease we have examined 28 different biomarkers in two cohorts of COVID-19 patients, aiming to systematically capture, quantify, and algorithmize how immune signals might be associated to the clinical outcome of COVID-19 patients.MethodsThe longitudinal concentration of 28 biomarkers of 95 COVID-19 patients was measured. We performed a dimensionality reduction analysis to determine meaningful biomarkers for explaining the data variability. The biomarkers were used as input of artificial neural network, random forest, classification and regression trees, k-nearest neighbors and support vector machines. Two different clinical cohorts were used to grant validity to the findings.ResultsWe benchmarked the classification capacity of two COVID-19 clinicals studies with different models and found that artificial neural networks was the best classifier. From it, we could employ different sets of biomarkers to predict the clinical outcome of COVID-19 patients. First, all the biomarkers available yielded a satisfactory classification. Next, we assessed the prediction capacity of each protein separated. With a reduced set of biomarkers, our model presented 94% accuracy, 96.6% precision, 91.6% recall, and 95% of specificity upon the testing data. We used the same model to predict 83% and 87% (recovered and deceased) of unseen data, granting validity to the results obtained.ConclusionsIn this work, using state-of-the-art computational techniques, we systematically identified an optimal set of biomarkers that are related to a prediction capacity of COVID-19 patients. The screening of such biomarkers might assist in understanding the underlying immune response towards inflammatory diseases.

【 授权许可】

CC BY