Critical Care

Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network ...

Predicting hospital admissions for upper respiratory tract complaints: An artificial neural network approach integrating air pollution and meteorological factors.

This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the correlation between hospital admissions for respiratory diseases and the levels of PM and SO pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is ...

Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study.

BACKGROUND: The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory ...

Multi-view heterogeneous graph learning with compressed hypergraph neural networks.

Multi-view learning is an emerging field of multi-modal fusion, which involves representing a single instance using multiple heterogeneous features to...

Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data.

INTRODUCTION: Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routi...

Machine learning-based prediction of vancomycin concentration after abdominal administration in patients with peritoneal dialysis-related peritonitis.

INTRODUCTION: Peritonitis is a serious complication of peritoneal dialysis (PD), in which insufficient control of antibacterial drug concentrations po...

ICURE: Intensive care unit (ICU) risk evaluation for 30-day mortality. Developing and evaluating a multivariable machine learning prediction model for patients admitted to the general ICU in Sweden.

BACKGROUND: A prediction model that estimates mortality at admission to the intensive care unit (ICU) is of potential benefit to both patients and soc...

Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns.

Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sle...

Retrospective analysis of interpretable machine learning in predicting ICU thrombocytopenia in geriatric ICU patients.

We developed an interpretable machine learning algorithm that prospectively predicts the risk of thrombocytopenia in older critically ill patients dur...

A machine-learning approach for stress detection using wearable sensors in free-living environments.

Stress is a psychological condition resulting from the body's response to challenging situations, which can negatively impact physical and mental heal...

Pre-gating and contextual attention gate - A new fusion method for multi-modal data tasks.

Multi-modal representation learning has received significant attention across diverse research domains due to its ability to model a scenario comprehe...

Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network.

Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep...

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electro...

ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.

BACKGROUND: Despite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsist...

Deriving Automated Device Metadata From Intracranial Pressure Waveforms: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury ICU Physiology Cohort Analysis.

IMPORTANCE: Treatment for intracranial pressure (ICP) has been increasingly informed by machine learning (ML)-derived ICP waveform characteristics. Th...

Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications.

There is a significant demand for multiplexed fluorescence sensing and detection across a range of applications. Yet, the development of portable and ...

Developing an explainable diagnosis system utilizing deep learning model: a case study of spontaneous pneumothorax.

The trend in the medical field is towards intelligent detection-based medical diagnostic systems. However, these methods are often seen as 'black boxe...

Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN).

Polypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disor...

Rapid and non-destructive identification of Panax ginseng origins using hyperspectral imaging, visible light imaging, and X-ray imaging combined with multi-source data fusion strategies.

The geographical origin of Panax ginseng significantly influences its nutritional value and chemical composition, which in turn affects its market pri...

Patient-ventilator asynchrony classification in mechanically ventilated patients: Model-based or machine learning method?

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchrony (PVA) is associated with poor clinical outcomes and remains under-monitored. Automated PVA det...