Critical Care

Sepsis

Latest AI and machine learning research in sepsis for healthcare professionals.

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Predicting presumed serious infection among hospitalized children on central venous lines with machine learning.

BACKGROUND: Presumed serious infection (PSI) is defined as a blood culture drawn and new antibiotic ...

AI-based mobile application to fight antibiotic resistance.

Antimicrobial resistance is a major global health threat and its development is promoted by antibiot...

Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) h...

Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.

This study aimed to develop a machine learning algorithm to identify key clinical measures to triag...

HeMA: A hierarchically enriched machine learning approach for managing false alarms in real time: A sepsis prediction case study.

Early detection of sepsis can be life-saving. Machine learning models have shown great promise in ea...

DeepAISE - An interpretable and recurrent neural survival model for early prediction of sepsis.

Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity...

Classification of COVID-19 by Compressed Chest CT Image through Deep Learning on a Large Patients Cohort.

Corona Virus Disease (COVID-19) has spread globally quickly, and has resulted in a large number of c...

HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes.

BACKGROUND: The spread of antibiotic resistance has become one of the most urgent threats to global ...

Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.

PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans play...

Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.

BACKGROUND: Timely identification of patients at a high risk of clinical deterioration is key to pri...

Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil.

The new coronavirus, which began to be called SARS-CoV-2, is a single-stranded RNA beta coronavirus,...

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is ...

Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods.

Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cel...

Accelerating Epidemiological Investigation Analysis by Using NLP and Knowledge Reasoning: A Case Study on COVID-19.

COVID-19 is threatening the health of the entire human population. In order to control the spread of...

Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.

The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serio...

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection an...

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable ...

Machine learning methods to improve bedside fluid responsiveness prediction in severe sepsis or septic shock: an observational study.

BACKGROUND: Passive leg raising (PLR) predicts fluid responsiveness in critical illness, although re...

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