Critical Care

Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases.

The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM is of great importance in terms of public health, medical resource allocation, and policy decision support. Recently, the machine learning methods bring promising solutions for ERV prediction in view of their powerful ability of short-term forecasting, while their performances still exist unkno...

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease.

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate p...

Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital.

BACKGROUND: Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of seve...

Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.

BACKGROUND: Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, ...

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute r...

Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images.

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of goo...

Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil.

The new coronavirus, which began to be called SARS-CoV-2, is a single-stranded RNA beta coronavirus, initially identified in Wuhan (Hubei province, Ch...

Oxynet: A collective intelligence that detects ventilatory thresholds in cardiopulmonary exercise tests.

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) stil...

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.

Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis, which is critical in reducing mortality, is challenging as ...

A U-Net based framework to quantify glomerulosclerosis in digitized PAS and H&E stained human tissues.

Reliable counting of glomeruli and evaluation of glomerulosclerosis in renal specimens are essential steps to assess morphological changes in kidney a...

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in -derived astrocytes ablated mice.

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) work...

A Clinically Practical and Interpretable Deep Model for ICU Mortality Prediction with External Validation.

Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it ...

Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients.

The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is...

Predicting Volume Responsiveness Among Sepsis Patients Using Clinical Data and Continuous Physiological Waveforms.

The efficacy of early fluid treatment in patients with sepsis is unclear and may contribute to serious adverse events due to fluid non-responsiveness....

[Artificial intelligence in neurocritical care].

Artificial intelligence (AI) has been introduced into medicine and an AI-assisted medicine will be the future that we should help to shape. In particu...

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

OBJECTIVE: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, ...

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis.

INTRODUCTION: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorize...

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to C...

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXR...