Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU

Frontiers in Immunology

Immunology

Ignacio Martin-Loeches¹  Rachael Cusack¹  Alexis Garduno¹  Jesus Francisco Bermejo-Martin²  Ali Toloue Ostadgavahi³  David Kelvin³  Abdullah Mahmud Al-Rafat³  Gustavo Sganzerla Martinez³ 

[1] Department of Clinical Medicine, Trinity College, University of Dublin, Dublin, Ireland;Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud de Castilla y León, Paseo de San Vicente, Salamanca, Spain;Universidad de Salamanca, C. Alfonso X el Sabio, s/n, Salamanca, Spain;Centro de Investigación Biomédica en Red en Enfermedades Respiratorias (CIBERES), CB22/06/00035, Instituto de Salud Carlos III, Avenida de Monforte de Lemos, Madrid, Spain;Laboratory of Emerging Infectious Diseases, Department of Immunology and Microbiology, Dalhousie University, Halifax, NS, Canada;Department of Pediatrics, Izaak Walton Killan (IWK) Health Center, CCfV, Halifax, NS, Canada;

关键词: biomarkers;    data mining;    pattern recocgnition;    artificial intelligence;    COVID - 19;    sepsis;    septic shock;   

DOI  :  10.3389/fimmu.2023.1137850

 received in 2023-01-04, accepted in 2023-02-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionMillions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients. MethodsIn our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool.ResultsWe isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients.DiscussionIn conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.

【 授权许可】

Unknown   
Copyright © 2023 Martinez, Ostadgavahi, Al-Rafat, Garduno, Cusack, Bermejo-Martin, Martin-Loeches and Kelvin