Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Subcategories: Sepsis
Showing 610-630 of 7,417 articles
Multi-task interaction learning for accurate segmentation and classification of breast tumors in ultrasound images.

In breast diagnostic imaging, the morphological variability of breast tumors and the inherent ambigu...

Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment.

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along wit...

Opportunistic access control scheme for enhancing IoT-enabled healthcare security using blockchain and machine learning.

The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to off...

Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score.

INTRODUCTION: Large learning models (LLMs) such as GPT are advanced artificial intelligence (AI) mod...

An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis.

BACKGROUND: Antimicrobial resistances pose significant challenges in health care systems. Clinical d...

Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.

Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be...

Prediction of zinc, cadmium, and arsenic in european soils using multi-end machine learning models.

Heavy metal contamination in soil is a major environmental and public health concern, especially in ...

Predictive modeling and optimization in dermatology: Machine learning for skin disease classification.

The accurate diagnosis of skin diseases is crucial for effective patient management and treatment, y...

Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates.

BACKGROUND: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of...

Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.

BACKGROUND: For preoxygenation, German guidelines consider non-invasive ventilation (NIV) as a possi...

Explainable machine learning model for predicting acute pancreatitis mortality in the intensive care unit.

BACKGROUND: Current prediction models are suboptimal for determining mortality risk in patients with...

Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center study.

BACKGROUND: Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic infla...

AI-powered prostate cancer detection: a multi-centre, multi-scanner validation study.

OBJECTIVES: Multi-centre, multi-vendor validation of artificial intelligence (AI) software to detect...

A hybrid network based on multi-scale convolutional neural network and bidirectional gated recurrent unit for EEG denoising.

Electroencephalogram (EEG) signals are time series data containing abundant brain information. Howev...

Multi-modal Language models in bioacoustics with zero-shot transfer: a case study.

Automatically detecting sound events with Artificial Intelligence (AI) has become increas- ingly pop...

Detection of human activities using multi-layer convolutional neural network.

Human Activity Recognition (HAR) plays a critical role in fields such as healthcare, sports, and hum...

Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.

OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patie...

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