Sepsis

Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.

Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified in randomized controlled trial cohorts using latent class analysis. Plasma biomarkers, key components in phenotype identification, currently lack point-of-care assays and represent a barrier to the clinical impleme...

A Time-Phased Machine Learning Model for Real-Time Prediction of Sepsis in Critical Care.

OBJECTIVES: As a life-threatening condition, sepsis is one of the major public health issues worldwide. Early prediction can improve sepsis outcomes with appropriate interventions. With the PhysioNet/Computing in Cardiology Challenge 2019, we aimed to develop and validate a machine learning algorithm with high prediction performance and clinical interpretability for prediction of sepsis onset duri...

[Clinical analysis of robot-assisted laparoscopic transabdominal preperitoneal inguinal hernia repair].

To explore the clinical value of robot-assisted laparoscopic transabdominal preperitoneal (TAPP) inguinal hernia repair. We performed a retrospectiv...

Antimicrobial and synergistic activity of thiazoline derivatives in combination with conventional antibiotics against multidrug resistant Staphylococcus aureus isolated from abscess drainage samples.

Emergence and spread of multidrug resistant (MDR) Staphylococcus aureus strains is becoming major challenge in treatment of soft tissue infections. Th...

A fatal invasive pulmonary infection in an adult patient with malignant lung adenocarcinoma.

BACKGROUND AND PURPOSE: complex as a ubiquitous environmental mold is increasingly reported to cause an invasive fungal infection in immunosuppressiv...

Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning.

Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate sc...

Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia.

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and...

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images.

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung...

Systematic analysis of supervised machine learning as an effective approach to predicate β-lactam resistance phenotype in Streptococcus pneumoniae.

Streptococcus pneumoniae is the most common human respiratory pathogen, and β-lactam antibiotics have been employed to treat infections caused by S. p...

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening conditio...

Deep transfer learning for video-based detection of newborn presence in incubator.

Preterm newborns are prone to late-onset sepsis, leading to a high risk of mortality. Video-based analysis of motion is a promising non-invasive appro...

The efficacy of support vector machines in modelling deviations from the Beer-Lambert law for optical measurement of lactate.

Lactate is an important biomarker with a significant diagnostic and prognostic ability in relation to life-threatening conditions and diseases such as...

Practical Machine Learning-Based Sepsis Prediction.

Sepsis is a life-threatening clinical syndrome and one of the most expensive conditions treated in hospitals. It is challenging to detect due to the n...

Potential Prognostic Markers in the Heart Rate Variability Features for Early Diagnosis of Sepsis in the Pediatric Intensive Care Unit using Convolutional Neural Network Classifiers.

Blood infection due to different circumstances could immediately develop to an extreme body reaction that leads to a serious life-threatening conditio...

Using Machine Learning Algorithms to Predict Antimicrobial Resistance and Assist Empirical Treatment.

Multi-drug-resistant (MDR) infections and their devastating consequences constitute a global problem and a constant threat to public health with immen...

Blood Lactate Concentration Prediction in Critical Care.

Blood lactate concentration is a reliable risk indicator of deterioration in critical care requiring frequent blood sampling. However, lactate measure...

Comparative outcomes of robot-assisted minimally invasive versus open esophagectomy in patients with esophageal squamous cell carcinoma: a propensity score-weighted analysis.

Robots are increasingly used in minimally invasive surgery. We evaluated the clinical benefits of robot-assisted minimally invasive esophagectomy (RAM...

Reinforcement Learning: Full Glass or Empty - Depends Who You Ask.

An extension of the prediction error theory of dopamine, imported from artificial intelligence, represents the full distribution over future rewards r...

On classifying sepsis heterogeneity in the ICU: insight using machine learning.

OBJECTIVES: Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of t...

[Prognostic model of small sample critical diseases based on transfer learning].

Aiming at the problem that the small samples of critical disease in clinic may lead to prognostic models with poor performance of overfitting, large p...