ABSTRACT 2 Background: The search for new biomarkers that allow an early diagnosis in sepsis has become a necessity in medicine. The objective of this study is to identify potential protein biomarkers of differential expression between sepsis and non - infectious systemic inflamm atory response syndrome (NISIRS). Methods: Prospective observational study of a cohort of septic patients activated by the Sepsis Code and patients admitted with NISIRS, during the period 2016 - 2017. A mass spectrometry - based approach was used to analyze the plasma proteins in the enrolled subjects . Subsequently, using recursive feature elimination (RFE) classification and cross - validation with a vector classifier, an association of these proteins in patients with sepsis compared to patients with NISIRS. The protein - protein interaction netwo rk was analyzed with String software. Results: A total of 277 patients (141 with sepsis and 136 with NISIRS) were included. Conclusion: There are proteomic patterns associated with sepsis compared to NISIRS with different strength of association. Advances in understanding these protein changes may allow for the identification of new biomarkers or therapeutic targets in the future. Key words: Sepsis, Septic shock, SIRS, Proteomics, Omics, Diagnosis INTRODUCTION 3 Sepsis is known as a clinical syndrome where life - threatening organ dysfunction occurs due to a dysregulated host response to infection.

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major one being acute kidney injury (AKI). For septic patients, approximately 50% develop AKI, with a mortality rate above 40%. Creating models that can accurately predict AKI based on specific qualities of septic patients is crucial for early detection and intervention. Using medical data from septic patients during intensive care unit (ICU) admission from the Medical Information Mart for Intensive Care 3 (MIMIC-III) database, we extracted 3301 patients with sepsis, with 73% of patients developing AKI. The data was randomly divided into a training set (n = 1980, 40%), a test set (n = 661, 10%), and a validation set (n = 660, 50%). The proposed model was logistic regression, and it was compared against five baseline models: XGBoost, K Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), and LightGBM. Area Under the Curve (AUC), Accuracy, F1-Score, and Recall were calculated for each model. After analysis, we were able to select 23 features to include in our model, the top features being urine output, maximum bilirubin, minimum bilirubin, weight, maximum blood urea nitrogen, and minimum estimated glomerular filtration rate. The logistic regression model performed the best, achieving an AUC score of 0.887 (95% CI: [0.861-0.915]), an accuracy of 0.817, an F1 score of 0.866, a recall score of 0.827, and a Brier score of 0.13. Compared to the best existing literature in this field, our model achieved an 8.57% improvement in AUC while using 13 fewer variables, showcasing its effectiveness in determining AKI in septic patients. While the features selected for predicting AKI in septic patients are similar to previous literature, the top features that influenced our model's performance differ.

The management of sepsis is a highly complex, multifaceted challenge that remains the realm of highly skilled and trained human experts. But as medical applications of artificial intelligence continue to pour in, it is becoming obvious that some of these decisions could soon be left to machines that could be dubbed "intelligent", improving clinical practice and patient outcomes [1]. Indeed, most of the tasks involved in the clinical management of sepsis (early recognition, selection of antibiotic therapy, haemodynamic optimisation, etc.) could be individually performed or optimised by dedicated algorithms. Most of what we call "artificial intelligence" is in fact machine learning--a set of computer tools intended to generate new knowledge from data [1]. Machine learning includes three categories of techniques: supervised (which uses labelled data to build a prediction model, for example for prognostication), unsupervised (which discovers patterns in data and generates clusters of subjects that share common characteristics) and reinforcement learning (where a sequential decision process is modelled and optimised). Below, I have selected a few significant applications that I consider the most likely to land in the clinical environment in the near future, either because of their robustness or their potential.

From 2017 to 2018 the number of scientific publications found via PubMed search using the keyword "Machine Learning" increased by 46% (4,317 to 6,307). The results of studies involving machine learning, artificial intelligence (AI), and big data have captured the attention of healthcare practitioners, healthcare managers, and the public at a time when Western medicine grapples with unmitigated cost increases and public demands for accountability. The complexity involved in healthcare applications of machine learning and the size of the associated data sets has afforded many researchers an uncontested opportunity to satisfy these demands with relatively little oversight. In a recent Nature Medicine article, "The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care," Komorowski and his coauthors propose methods to train an artificial intelligence clinician to treat sepsis patients with vasopressors and IV fluids. In this post, we will closely examine the claims laid out in this paper. In particular, we will study the individual treatment profiles suggested by their AI Clinician to gain insight into how their AI Clinician intends to treat patients on an individual level.