AI Models Enhance Predictions for Chronic Kidney Disease Progression
AI models improve predictions for chronic kidney disease progression, aiding early detection and management. 🩺📈
Deep learning model using cross-sequence learning to identify orbital fractures in radiographs of patients under 20 Years.
Deep learning model detects orbital fractures in youth radiographs: AUROC 0.802, sensitivity 65.8%, specificity 86.5%. 📊👁️
Improved Digital ECG Access at Barts Health Enhances Patient Care
Digital ECG access at Barts Health improves patient care, enabling faster decisions and reducing errors. Enhanced efficiency benefits all. 📈❤️
Social Media Recruitment in Indigenous and Native American Populations: Challenges in the AI Age.
Social media recruitment for Indigenous populations faces AI challenges. Limited success, data quality issues, and effective strategies explored. 📊🤝
Importance of Mentorship in Digital Health Leadership
Mentorship in digital health is vital for career growth and collaboration. It fosters confidence and innovation in challenging environments. 🌐🤝
Predicting severe renal dysfunction in alcohol-associated cirrhosis: Comparative performance of liver function scores and machine learning models.
Predicting severe renal dysfunction in cirrhosis: MELD score shows 80% prevalence at high levels. Machine learning enhances accuracy! 📊🔍
Remote MRI Scanning Pilot Reduces Patient Wait Times and DNA Rates
Remote MRI scanning pilot reduces patient wait times and DNA rates, enhancing flexibility and training efficiency. 🏥📉
Comparative Evaluation of Clinical Large Language Models and Machine Learning to Predict Antimicrobial Resistance in Hospital-Onset Sepsis.
AI vs. Traditional ML in Predicting Antimicrobial Resistance: LLMs show 90%+ negative predictive value! 📊💉
Patients Know Best Receives Investment to Enhance Patient Record System
Patients Know Best secures investment from DNV to enhance its personal health record system and expand internationally. 🌍📈
Machine Learning Approach for Frailty Detection in Long-Term Care Using Accelerometer-Measured Gait and Daily Physical Activity: Model Development and Validation Study.
Machine learning detects frailty in long-term care with 86.3% accuracy using accelerometer data. 📊👵 #HealthTech