Critical Care

Clinical management of sepsis can be improved by artificial intelligence: yes.

Multi-objective ensemble deep learning using electronic health records to predict outcomes after lung cancer radiotherapy.

Accurately predicting treatment outcome is crucial for creating personalized treatment plans and follow-up schedules. Electronic health records (EHRs) contain valuable patient-specific information that can be leveraged to improve outcome prediction. We propose a reliable multi-objective ensemble deep learning (MoEDL) method that uses features extracted from EHRs to predict high risk of treatment f...

Evaluation of Amikacin Pharmacokinetics in Critically Ill Patients with Intra-abdominal Sepsis.

Although the current widespread use of amikacin is in intra-abdominal sepsis treatment, its pharmacokinetic changes in the present setting are not ye...

Computational modeling of the monoaminergic neurotransmitter and male neuroendocrine systems in an analysis of therapeutic neuroadaptation to chronic antidepressant.

Second-line depression treatment involves augmentation with one (rarely two) additional drugs, of chronic administration of a selective serotonin reup...

Multi-view ensemble learning with empirical kernel for heart failure mortality prediction.

Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human hea...

Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning.

UNLABELLED: Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO),...

Usefulness of presepsin as diagnostic and prognostic marker of sepsis in daily clinical practice.

INTRODUCTION: Sepsis represents a major cause of morbidity and mortality in critically ill patients. Early diagnosis and appropriate treatment have a ...

Protocol for the Effectiveness of an Anesthesiology Control Tower System in Improving Perioperative Quality Metrics and Clinical Outcomes: the TECTONICS randomized, pragmatic trial.

Perioperative morbidity is a public health priority, and surgical volume is increasing rapidly. With advances in technology, there is an opportunity ...

Verification with the utility of an established rapid assessment of brain safety for newly developed vaccines.

In the twenty-first century, high contagious infectious diseases such as SARS (Severe Acute Respiratory Syndrome), MERS (Middle East Respiratory Syndr...

Machine learning-based dynamic mortality prediction after traumatic brain injury.

Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensi...

The low-protein diet for chronic kidney disease: 8 years of clinical experience in a nephrology ward.

BACKGROUND: Guidelines indicate that a low-protein diet (LPD) delays dialysis in severe chronic kidney disease (CKD). We assessed the value of these g...

Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which re...

In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning.

In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its...

Machine Learning Models for Analysis of Vital Signs Dynamics: A Case for Sepsis Onset Prediction.

OBJECTIVE: Achieving accurate prediction of sepsis detection moment based on bedside monitor data in the intensive care unit (ICU). A good clinical ou...

Poly(ethylene glycol)-poly(ε-caprolactone)-based micelles for solubilization and tumor-targeted delivery of silibinin.

Silibinin is a naturally occurring compound with known positive impacts on prevention and treatment of many types of in general and cancer in partic...

Comparison of Automated Sepsis Identification Methods and Electronic Health Record-based Sepsis Phenotyping: Improving Case Identification Accuracy by Accounting for Confounding Comorbid Conditions.

UNLABELLED: To develop and evaluate a novel strategy that automates the retrospective identification of sepsis using electronic health record data.

Using machine learning to selectively highlight patient information.

BACKGROUND: Electronic medical record (EMR) systems need functionality that decreases cognitive overload by drawing the clinician's attention to the r...

Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study.

A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease pati...

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation.

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast...

Machine Learning Reveals the Texture of Regional Lung Ventilation at CT.