Sepsis

Machine Learning Study of Metabolic Networks ChEMBL Data of Antibacterial Compounds.

Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algori...

COVID-19 Isolation Control Proposal UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls.

Artificial intelligence researchers conducted different studies to reduce the spread of COVID-19. Unlike other studies, this paper isn't for early infection diagnosis, but for preventing the transmission of COVID-19 in social environments. Among the studies on this is regarding social distancing, as this method is proven to prevent COVID-19 to be transmitted from one to another. In the study, Robo...

A three-methylation-driven gene-based deep learning model for tuberculosis diagnosis in patients with and without human immunodeficiency virus co-infection.

Improved diagnostic tests for tuberculosis (TB) among people with human immunodeficiency virus (HIV) are urgently required. We hypothesized that methy...

Fear Detection in Multimodal Affective Computing: Physiological Signals versus Catecholamine Concentration.

Affective computing through physiological signals monitoring is currently a hot topic in the scientific literature, but also in the industry. Many wea...

Machine Learning Models for the Diagnosis and Prognosis Prediction of High-Grade B-Cell Lymphoma.

High-grade B-cell lymphoma (HGBL) is a newly introduced category of rare and heterogeneous invasive B-cell lymphoma (BCL), which is diagnosed dependin...

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires.

The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immun...

Decoding the dopamine transporter imaging for the differential diagnosis of parkinsonism using deep learning.

PURPOSE: This work attempts to decode the discriminative information in dopamine transporter (DAT) imaging using deep learning for the differential di...

Leveraging clinical data across healthcare institutions for continual learning of predictive risk models.

The inherent flexibility of machine learning-based clinical predictive models to learn from episodes of patient care at a new institution (site-specif...

A Deep Learning Approach to Estimate the Incidence of Infectious Disease Cases for Routinely Collected Ambulatory Records: The Example of Varicella-Zoster.

The burden of infectious diseases is crucial for both epidemiological surveillance and prompt public health response. A variety of data, including tex...

Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis?

OBJECTIVE: To analyze the critical alarms predictors of clinical deterioration/sepsis for clinical decision making in patients admitted to a reference...

Challenging molecular dogmas in human sepsis using mathematical reasoning.

Sepsis is defined as a dysregulated host-response to infection, across all ages and pathogens. What defines a dysregulated state remains intensively r...

Diagnosis of COVID-19 patients by adapting hyper parametertuned deep belief network using hosted cuckoo optimization algorithm.

COVID-19 is an infection caused by recently discovered corona virus. The symptoms of COVID-19 are fever, cough and dumpiness of breathing. A quick and...

Machine Learning-Assisted Preoperative Diagnosis of Infection Stones in Urolithiasis Patients.

The decision-making of how to treat urinary infection stones was complicated by the difficulty in preoperative diagnosis of these stones. Hence, we d...

Study on transfer learning capabilities for pneumonia classification in chest-x-rays images.

BACKGROUND: over the last year, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants have highlighted the importance of s...

Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach.

BACKGROUND: Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsi...

Antilogic, a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species.

OBJECTIVE: Antibiotic susceptibility testing (AST) is necessary in order to adjust empirical antibiotic treatment, but the interpretation of results r...

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.

The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiol...

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU.

BACKGROUND: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stab...

Sepsis labels defined by claims-based methods are ill-suited for training machine learning algorithms.

A Machine Learning Model for Early Prediction and Detection of Sepsis in Intensive Care Unit Patients.

In today's scenario, sepsis is impacting millions of patients in the intensive care unit due to the fact that the mortality rate is increased exponent...