Critical Care

[Predicting prolonged length of intensive care unit stay machine learning].

OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models support vector machine (SVM), classification and regression tree (CART), and random forest (RF), and to compare the prediction perfor-mance of the three machine learning models with the customized simplified acute physiology score Ⅱ(SAPS-Ⅱ) model.

Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning.

In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-making involving patients with severe trauma. We aimed to develope a machine learning-based early prognostic model based on admission features and initial ED management.We only recruited patients with severe trauma (defined as an injury severity score >1...

Phase2Phase: Respiratory Motion-Resolved Reconstruction of Free-Breathing Magnetic Resonance Imaging Using Deep Learning Without a Ground Truth for Improved Liver Imaging.

OBJECTIVES: Respiratory binning of free-breathing magnetic resonance imaging data reduces motion blurring; however, it exacerbates noise and introduce...

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury.

PURPOSE OF REVIEW: Acute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory...

Vitamin D and COVID-19 - Let's Explore the Relationship!

Vitamin D plays a protective role against COVID-19. Patients with deficiency of vitamin D are more prone to severe SARS-CoV-2 infections. It is known ...

Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolutional Neural Networks.

Large annotated lung sound databases are publicly available and might be used to train algorithms for diagnosis systems. However, it might be a challe...

Detecting Uncertainty of Mortality Prediction Using Confident Learning.

Early mortality prediction is an actively researched problem that has led to the development of various severity scores and machine learning (ML) mode...

A Machine Learning Understanding of Sepsis.

Sepsis is a serious cause of morbidity and mortality and yet its pathophysiology remains elusive. Recently, medical and technological advances have he...

Perioperative Risk Assessment in Pancreatic Surgery Using Machine Learning.

Pancreatic surgery is associated with a high risk for postoperative complications and death of patients. Complications occur in a variable interval af...

Exploring Features Contributing to the Early Prediction of Sepsis Using Machine Learning.

The increasing availability of electronic health records and administrative data and the adoption of computer-based technologies in healthcare have si...

Surgical instrument segmentation based on multi-scale and multi-level feature network.

Surgical instrument segmentation is critical for the field of computer-aided surgery system. Most of deep-learning based algorithms only use either mu...

An Interpretable Intensive Care Unit Mortality Risk Calculator.

Mortality risk is a major concern to patients who have just been discharged from the intensive care unit (ICU). Many studies have been directed to con...

Design of Novel End-effectors for Robot-assisted Swab Sampling to Combat Respiratory Infectious Diseases.

The COVID-19 outbreak has caused the mortality worldwide and the use of swab sampling is a common way of screening and diagnosis. To combat respirator...

Are small airplanes safe with regards to COVID-19 transmission?

Social distancing is impossible in small aircrafts but there is a specific, highly effective ventilation system. Measurements proved that there is no ...

Algorithmic prognostication in critical care: a promising but unproven technology for supporting difficult decisions.

PURPOSE OF REVIEW: Patients, surrogate decision makers, and clinicians face weighty and urgent decisions under uncertainty in the ICU, which could be ...

Interpretable Machine Learning Model for Early Prediction of Mortality in ICU Patients with Rhabdomyolysis.

PURPOSE: Rhabdomyolysis (RM) is a complex set of clinical syndromes that involves the rapid dissolution of skeletal muscles. Mortality from RM is appr...

Do you have COVID-19? An artificial intelligence-based screening tool for COVID-19 using acoustic parameters.

This study aimed to develop an artificial intelligence (AI)-based tool for screening COVID-19 patients based on the acoustic parameters of their voice...

Cardiovascular RNA markers and artificial intelligence may improve COVID-19 outcome: a position paper from the EU-CardioRNA COST Action CA17129.

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 Feb...

Temporal Differential Expression of Physiomarkers Predicts Sepsis in Critically Ill Adults.

BACKGROUND: Sepsis is a life-threatening condition with high mortality rates. Early detection and treatment are critical to improving outcomes. Our pr...

Deep Learning-Based Nuclear Lobe Count Method for Differential Count of Neutrophils.

Differentiating neutrophils based on the count of nuclear lobulation is useful for diagnosing various hematological disorders, including megaloblastic...