Critical Care

A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.

BACKGROUND AND OBJECTIVE: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.

Potential of Parameters of Iron Metabolism for the Diagnosis of Anemia of Inflammation in the Critically Ill.

BACKGROUND: Anemia of inflammation (AI) is the most common cause of anemia in the critically ill, but its diagnosis is a challenge. New therapies specific to AI are in development, and they require accurate detection of AI. This study explores the potential of parameters of iron metabolism for the diagnosis of AI during an ICU stay.

Analysis of parameters affecting blood oxygen saturation and modeling of fuzzy logic system for inspired oxygen prediction.

BACKGROUND AND OBJECTIVE: Fraction of Inspired Oxygen is one of the arbitrary set ventilator parameters which has critical influence on the concentrat...

Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs.

OBJECTIVE: Sepsis remains a costly and prevalent syndrome in hospitals; however, machine learning systems can increase timely sepsis detection using e...

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

OBJECTIVES: Manual clinical scoring systems are the current standard used for acute asthma clinical care pathways. No automated system exists that ass...

Refining humane endpoints in mouse models of disease by systematic review and machine learning-based endpoint definition.

Ideally, humane endpoints allow for early termination of experiments by minimizing an animal's discomfort, distress and pain, while ensuring that scie...

Using the Shapes of Clinical Data Trajectories to Predict Mortality in ICUs.

UNLABELLED: 1) To show how to exploit the information contained in the trajectories of time-varying patient clinical data for dynamic predictions of m...

Remote Control of Greenhouse Vegetable Production with Artificial Intelligence-Greenhouse Climate, Irrigation, and Crop Production.

The global population is increasing rapidly, together with the demand for healthy fresh food. The greenhouse industry can play an important role, but ...

Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of pa...

Towards real-time respiratory motion prediction based on long short-term memory neural networks.

Radiation therapy of thoracic and abdominal tumors requires incorporating the respiratory motion into treatments. To precisely account for the patient...

An Equation Based on Fuzzy Mathematics to Assess the Timing of Haemodialysis Initiation.

In order to develop an equation that integrates multiple clinical factors including signs and symptoms associated with uraemia to assess the initiatio...

Inverse reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units.

BACKGROUND: Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To...

Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU.

Studies reveal that the false alarm rate (FAR) demonstrated by intensive care unit (ICU) vital signs monitors ranges from 0.72 to 0.99. We applied mac...

Intensive Care Unit Telemedicine in the Era of Big Data, Artificial Intelligence, and Computer Clinical Decision Support Systems.

This article examines the history of the telemedicine intensive care unit (tele-ICU), the current state of clinical decision support systems (CDSS) in...

Heterogeneous effects of alveolar recruitment in acute respiratory distress syndrome: a machine learning reanalysis of the Alveolar Recruitment for Acute Respiratory Distress Syndrome Trial.

BACKGROUND: Despite a robust physiological rationale, recruitment manoeuvres with PEEP titration were associated with harm in the Alveolar Recruitment...

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender.

This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respira...

Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination.

Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experie...

Machine learning for patient risk stratification for acute respiratory distress syndrome.

BACKGROUND: Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-...

Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks.

First and second ventilatory thresholds (VT and VT) represent the boundaries of the moderate-heavy and heavy-severe exercise intensity. Currently, VTs...

Discriminative multi-source adaptation multi-feature co-regression for visual classification.

Learning an effective visual classifier from few labeled samples is a challenging problem, which has motivated the multi-source adaptation scheme in m...