Intensivists

What every intensivist should know about Big Data and targeted machine learning in the intensive care unit.

Dynamic Features Impact on the Quality of Chronic Heart Failure Predictive Modelling.

We study the way dynamics affects modelling in chronic heart failure (CHF) tasks. By dynamics we und...

Artificial Intelligence Tools for Sepsis and Cancer.

Effects of Midazolam and Midazolam-Butorphanol on Gastrointestinal Transit Time and Motility in Cockatiels ().

Positive contrast gastrointestinal (GI) studies are performed frequently in avian medicine to identi...

Hydrocortisone, Vitamin C and thiamine for the treatment of sepsis and septic shock following cardiac surgery.

BACKGROUND AND AIMS: The effect of vitamin C on vasopressor requirement in critically ill patients h...

The role of presepsin in the diagnosis and assessment of severity of sepsis and severe pneumonia.

AIM: The aim of this study was to evaluate marker of inflammation presepsin to improve diagnosis of ...

Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to pred...

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and d...

Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks.

This paper addresses issues of brain tumor, glioma, grading from multi-sensor images. Different type...

Evaluating ICU Clinical Severity Scoring Systems and Machine Learning Applications: APACHE IV/IVa Case Study.

Clinical scoring systems have been developed for many specific applications, yet they remain underut...

Early Prediction of Sepsis in EMR Records Using Traditional ML Techniques and Deep Learning LSTM Networks.

Sepsis is a life-threatening condition caused by infection and subsequent overreaction by the immune...

Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading.

Diabetic Retinopathy (DR) is a non-negligible eye disease among patients with Diabetes Mellitus, and...

Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images.

Lung cancer is one of the four major cancers in the world. Accurate diagnosing of lung cancer in the...

Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation.

Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR...

Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve p...

Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients.

OBJECTIVES: Risk adjustment algorithms for ICU mortality are necessary for measuring and improving I...

Articulated Multi-Instrument 2-D Pose Estimation Using Fully Convolutional Networks.

Instrument detection, pose estimation, and tracking in surgical videos are an important vision compo...

[Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].

OBJECTIVE: To construct an in-hospital mortality prediction model for patients with acute kidney inj...

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

OBJECTIVES: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critica...

A review on machine learning principles for multi-view biological data integration.

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong...

Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition.

This paper explores multi-task learning (MTL) for face recognition. First, we propose a multi-task c...