Critical Care

Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study.

BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient conditions. This study aimed to develop and externally validate machine learning models for predicting malnutrition within 24 h of intensive care unit (...

The emerging role of artificial intelligence in heart failure.

Heart Failure is a prevalent disease with significant impacts on morbidity and mortality. Heart failure patients have a large volume of healthcare data which is digitized and can be collated. Artificial intelligence (AI) can then be used to assess the data for underlying patterns. AI systems can be trained to analyze readily available data, such as ECGs and heart sounds, and assess likelihood of h...

Deep learning framework for cardiorespiratory disease detection using smartphone IMU sensors.

Respiratory and cardiovascular diseases represent a significant global health burden, underscoring the need for innovative, accessible, and cost-effec...

Efficient sleep apnea detection using single-lead ECG: A CNN-Transformer-LSTM approach.

BACKGROUND: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respiratory patterns during sleep. This disruption can hav...

Predicting Unplanned Intubations in Rib Fracture Patients: An Interpretable Machine Learning Approach.

BackgroundTraumatic rib fractures can lead to respiratory complications necessitating unplanned intubation, but predictors have been inadequately deli...

Machine learning for the prediction of urosepsis using electronic health record data.

Urosepsis, a medical condition resulting from the progression of urinary tract infection (UTI), is a leading cause of death in the US. Urosepsis occur...

Reconstruction of Heart-related Imaging from Lung Electrical Impedance Tomography Using Semi-Siamese U-Net.

INTRODUCTION: Electrical Impedance Tomography (EIT) is widely used for bedside ventilation monitoring but is limited in reconstructing cardiac-related...

Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.

Community-acquired pneumonia (CAP) is associated with high mortality, and accurate diagnosis and risk prediction are essential for improving patient o...

Predicting and Diagnosing Pneumonia in Patients Undergoing Elective Cardiac Surgery via Machine Learning Analysis of Exhaled Volatile Carbonyl Compounds.

OBJECTIVE: Pneumonia remains one of the most common post-operative complications after elective cardiac surgery. Early intervention could lead to impr...

Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.

Heart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumo...

Enhancing gas concentration prediction and ventilation efficiency in deep coal mines: a hybrid DL-Koopman and Fuzzy-PID framework.

With the increasing depth of coal mining operations, traditional ventilation systems are becoming insufficient to address the growing safety and opera...

Hybrid Harris hawks-optimized random forest model for detecting multi-element geochemical anomalies related to mineralization.

Reliable recognition of geochemical anomalies linked to ore deposits is one of the most significant challenges in mineral exploration. Several advance...

Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network.

To address misdiagnosis caused by feature coupling in multi-label medical image classification, this study introduces a chest X-ray pathology reasonin...

Machine learning model and hemoglobin to red cell distribution width ratio evaluates all-cause mortality in pulmonary embolism.

The ratio of hemoglobin (Hb) to red blood cell distribution width (RDW), known as HRR, functions as an innovative indicator related to prognosis. Howe...

Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.

Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leadi...

The Role of Precision Nutrition in Kidney Disease.

Precision nutrition utilizes an individualized approach in which dietary interventions are tailored according to patients' genetic, biologic, and envi...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiologic...

Predicting long-term kidney graft failure using novel multi-omic blood-based biomarkers and artificial intelligence tools.

Kidney transplantation (KT) remains the preferred treatment for end-stage renal disease. With advancements in immunosuppressive regimens and KT survei...

Enhancing anomaly detection and prevention in Internet of Things (IoT) using deep neural networks and blockchain based cyber security.

The rapid adoption of Internet of Things (IoT) devices has significantly increased cybersecurity risks, making them vulnerable to anomalies, attacks, ...

Identifying real time surveillance indicators to estimate COVID-19 hospital admissions in Colorado during and after the public health emergency.

Questions remain about how best to focus surveillance efforts for COVID-19 and other emerging respiratory diseases. We used an archive of COVID-19 dat...