Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU - Martinez, Gustavo Sganzerla; Ostadgavahi, Ali Toloue; Al-Rafat, Abdullah Mahmud; Garduno, Alexis; Cusack, Rachael; Bermejo-Martin, Jesus Francisco; Martin-Loeches, Ignacio; Kelvin, David (Frontiers in Immunology, 2023)
Millions 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 plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that that the severity of sepsis and septic shock patients is a function of the concentration of biomarkers of patients. In 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. We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The up regulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients. In conclusion, we built a function considering biomarkers 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, favouring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.