Critical Care

Deep reinforcement learning for multi-targets propofol dosing.

The administration of propofol for sedation or general anesthesia presents challenges due to the complex relationship between patient factors and real-time physiological responses. This study explores the application of deep reinforcement learning (DRL) to automate propofol dosing, aiming to maintain multiple physiological parameters including bispectral index (BIS), heart rate (HR), respiratory r...

Leveraging Artificial Intelligence to Reduce Neuroscience ICU Length of Stay.

GOAL: Efficient patient flow is critical at Tampa General Hospital (TGH), a large academic tertiary care center and safety net hospital with more than 50,000 discharges and 30,000 surgical procedures per year. TGH collaborated with GE HealthCare Command Center to build a command center (called CareComm) with real-time artificial intelligence (AI) applications, known as tiles, to dynamically stream...

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using se...

Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient management and quality improvement in healthcare. H...

Machine Learning-Based Prediction of Delirium and Risk Factor Identification in Intensive Care Unit Patients With Burns: Retrospective Observational Study.

BACKGROUND: The incidence of delirium in patients with burns receiving treatment in the intensive care unit (ICU) is high, reaching up to 77%, and has...

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 accessible in remote locations far from populatio...

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 decision support systems (CDSSs) represent a potent...

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 offer patient/user-related services that are ubiquito...

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 ventilator-associated pneumonia (VAP) can optimiz...

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 acute pancreatitis (AP); this might be improved b...

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 possible method in prehospital trauma care in the absen...

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 inflammation and coagulopathy. This study aimed to deve...

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 popular in the field of bioacoustics, ecoacoustics, a...

Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.

OBJECTIVES: To develop a machine learning-based software as a medical device to predict the endurance and outcomes of peritoneal dialysis (PD) patient...

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 patients with acute gastrointestinal bleeding (AGIB) in...

Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion...

Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model.

BACKGROUND: Agitation and sedation management is critical in intensive care as it affects patient safety. Traditional nursing assessments suffer from ...

Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on the MIMIC database.

OBJECTIVE: This study developed and validated a stacked ensemble machine learning model to predict the risk of acute kidney injury in patients with ac...

Prioritisation of functional needs for ICU intelligent robots in China: a consensus study based on the national survey and nominal group technique.

OBJECTIVE: This study aims to define the prioritisation of the needs for an intelligent robot's functions in the intensive care unit (ICU) from a clin...

Imbalanced Power Spectral Generation for Respiratory Rate and Uncertainty Estimations Based on Photoplethysmography Signal.

Respiratory rate (RR) changes in the elderly can indicate serious diseases. Thus, accurate estimation of RRs for cardiopulmonary function is essential...