Intensivists

Prospect of using deep learning for predicting differentiation of myeloid progenitor cells after sepsis.

Comparing Machine Learning Algorithms for Predicting Acute Kidney Injury.

Prior studies have used vital signs and laboratory measurements with conventional modeling techniques to predict acute kidney injury (AKI). The purpose of this study was to use the trend in vital signs and laboratory measurements with machine learning algorithms for predicting AKI in ICU patients. The eICU Collaborative Research Database was queried for five consecutive days of laboratory measurem...

New Phenotypes for Sepsis: The Promise and Problem of Applying Machine Learning and Artificial Intelligence in Clinical Research.

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 understand the patient history and the appearance of ...

Artificial Intelligence Tools for Sepsis and Cancer.

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 have been evaluated previously. We aimed to evaluat...

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 identify GI obstruction, luminal distension, and intraco...

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 severe pneumonia, sepsis.

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 predict the onset of severe sepsis in critically ill c...

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 patient triage and prompt conversations about patie...

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 underutilized for common reasons such as model inaccuracy...

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 ICU performance. Existing risk adjustment algorithm...

[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 injury (AKI) in intensive care unit (ICU) by using su...

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 critically ill patients. Early intervention with antibiot...

Procalcitonin in inflammatory bowel disease: Drawbacks and opportunities.

The measurement of procalcitonin has recently become a mainstay for the diagnosis and therapeutic management of severe bacterial infections, especiall...

[Ulinastatin attenuates hyper-permeability of vascular endothelialium cells induced by serum from patients with sepsis].

Objective To study the effect of ulinastatin (UTI) on the hyper-permeability of human umbilical vein endothelial cell induced by the serum from patien...

Feasibility of Automated Propofol Sedation for Transcatheter Aortic Valve Implantation: A Pilot Study.

BACKGROUND: Recently, several trials have shown that closed-loop sedation is feasible. No study has used automated sedation in extremely frail patient...

Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.

OBJECTIVES: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care thr...

Discriminating sample groups with multi-way data.

High-dimensional linear classifiers, such as distance weighted discrimination (DWD) and versions of the support vector machine (SVM), are commonly use...