Critical Care

Surgical management of secondary hyperparathyroidism in dialysis patients in Senegal.

Secondary hyperparathyroidism (SHPT) is a common complication of end-stage renal disease. Surgical management occurs in severe forms and/or unresponsive medical treatment. The aim of this study was to outline the indications of parathyroidectomy and its evolution after surgical approach. It was a five-year multicenter backward study in Otorhinolaryngology Department of Fann Hospital and four dialy...

Bariatric surgery and hemodialysis: A case report of a patient with less 40 kilograms and without dialysis.

Obesity is an important risk factor for chronic kidney disease. Bariatric surgery (BS) may improve long-term kidney outcomes, but there is no agreement regarding the benefit and timing of BS in patients with end-stage renal disease. We present a 46-year-old male patient with type 2 diabetes mellitus, in hemodialysis (HD), and with a body mass index (BMI) of 39.79 kg/m, who was submitted to laparos...

Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.

OBJECTIVE: To compare the performance of machine learning models against the traditionally derived Combined Assessment of Risk Encountered in Surgery ...

Artificial intelligence to guide management of acute kidney injury in the ICU: a narrative review.

PURPOSE OF REVIEW: Acute kidney injury (AKI) frequently complicates hospital admission, especially in the ICU or after major surgery, and is associate...

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

OBJECTIVES: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is ...

Early Prediction of Sepsis From Clinical Data Using Ratio and Power-Based Features.

OBJECTIVES: Early prediction of sepsis is of utmost importance to provide optimal care at an early stage. This work aims to deploy soft-computing and ...

A Case of Dapsone-induced Mild Methemoglobinemia with Dyspnea and Cyanosis.

Dear Editor, Dapsone is a dual-function drug with antimicrobial and antiprotozoal effects and anti-inflammatory features (1). In dermatology, it is a ...

Latent COVID-19 Clusters in Patients with Chronic Respiratory Conditions.

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chroni...

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 treatmen...

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 w...

Machine intelligence for early targeted precision management and response to outbreaks of respiratory infections.

OBJECTIVES: To evaluate the utility of machine learning (ML) for the management of Medicare beneficiaries at risk of severe respiratory infections in ...

Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.

BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status...

Advances in the rehabilitation of intensive care unit acquired weakness: A case report on the promising use of robotics and virtual reality coupled to physiotherapy.

INTRODUCTION: Traditional physiotherapy is currently the best approach to manage patients with intensive care unit acquired weakness (ICUAW). We repor...

Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.

OBJECTIVES: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence alg...

A machine learning method for automatic detection and classification of patient-ventilator asynchrony.

Patients suffering from respiratory failure are often put on assisted mechanical ventilation. Patient-ventilator asynchrony (PVA) can occur during mec...

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized ...

Robust Deep Learning Framework For Predicting Respiratory Anomalies and Diseases.

This paper presents a robust deep learning framework developed to detect respiratory diseases from recordings of respiratory sounds. The complete dete...

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...