Critical Care

Lessons in machine learning model deployment learned from sepsis.

In three recent and related publications, researchers from Johns Hopkins University and Bayesian Health report results from implementing and prospectively evaluating the Targeted Real-time Early Warning System (TREWS) for sepsis at five hospitals..

[The future patient monitoring in the bed ward].

Current monitoring of vital signs in hospital wards rely on infrequent manual measurements. This narrative review describes how new wearable devices with artificial intelligence interpretation may overcome this challenge by providing nurses with continuous data without inducing alarm fatigue. Severe complications in non-ICU hospital wards are commonly preceded by vital sign deviations, and an idea...

Development and validation of a deep learning model to predict the survival of patients in ICU.

BACKGROUND: Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the surviv...

Fuzzy-Based Expert Supervision System for Feedback Controlled Oxygenation.

Supervision of mechanical ventilation is currently still performed by clinical staff. With the increasing level of automation in the intensive care un...

CapNet: A Deep Learning-based Framework for Estimation of Capnograph Signal from PPG.

Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a patient with respiratory deficiency. The capnogra...

An Ensemble of Deep Learning Frameworks for Predicting Respiratory Anomalies.

This paper evaluates a range of deep learning frameworks for detecting respiratory anomalies from input audio. Audio recordings of respiratory cycles ...

Multi-Expert Deep Networks for Multi-Disease Detection in Retinal Fundus Images.

Automatic diagnosis of eye diseases from retinal fundus images is quite challenging. Common public datasets include images of subjects with multiple d...

Testing robot-based assist-as-needed therapy for improving active participation of a patient during early neurorehabilitation: a case study.

In this study, a patient in the Intensive Care-Unit received robot-based mobilization therapy with an assist-as-needed (AAN) function over the course ...

Treatment Prediction in the ICU Setting Using a Partitioned, Sequential Deep Time Series Analysis.

We developed a neural network architecture to evaluate the patient's state using temporal data, patient's demographics and comorbidities. We examined ...

Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records.

OBJECTIVE: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in ...

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis.

BACKGROUND: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic perform...

Enhancing sepsis management through machine learning techniques: A review.

Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guid...

Rapid SARS-CoV-2 Inactivation in a Simulated Hospital Room Using a Mobile and Autonomous Robot Emitting Ultraviolet-C Light.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since 2019 has made mask-wearing, physical distancing, hygiene, and disinfe...

Invited Commentary: "Robotic Airway Resection Under Spontaneous Ventilation".

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.

Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is...

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various predictio...

A roadmap for multi-omics data integration using deep learning.

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data ha...

An Efficient Method for Coronavirus Detection Through X-rays Using Deep Neural Network.

BACKGROUND: Coronavirus (COVID-19) is a group of infectious diseases caused by related viruses called coronaviruses. In humans, the seriousness of inf...

Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The ele...

Deep-Learning Model Based on Convolutional Neural Networks to Classify Apnea-Hypopnea Events from the Oximetry Signal.

Automated analysis of the blood oxygen saturation (SpO) signal from nocturnal oximetry has shown usefulness to simplify the diagnosis of obstructive s...