Identification of hub biomarkers in coronary artery disease patients using machine learning and bioinformatic analyses.
Machine learning reveals 11 hub biomarkers for coronary artery disease, highlighting ITM2B's potential role in inflammation. 🫀🔬
Children’s Health Ireland Enhances Diagnostic Imaging with Sectra Partnership
Children's Health Ireland partners with Sectra to improve diagnostic imaging access, enhancing care for young patients. 🏥📸
Practical Approach for Evaluating Machine Learning Anomaly Detection Algorithms for Epidemic Early Warning Systems.
Evaluating machine learning in epidemic early warning systems: promising results from LSTM and Isolation Forest models. 📊🔍
Digital twins for the era of personalized surgery.
Exploring digital twins in personalized surgery: enhancing precision and training for better outcomes. 🤖🔍
Benchmarking the Confidence of Large Language Models in Answering Clinical Questions: Cross-Sectional Evaluation Study.
Evaluating LLMs' confidence in clinical answers reveals paradoxical overconfidence in lower-performing models. 📊🤖
AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts.
AI's role in MRI: Data augmentation strategies improve segmentation performance despite motion artifacts. 📊🧠
NHS Suppliers Encouraged to Adopt Cyber Security Best Practices
NHS suppliers urged to adopt cyber security best practices for enhanced protection. Collaboration is key to safeguarding healthcare. 🔒🩺
From E-Patients to AI Patients: The Tidal Wave Empowering Patients, Redefining Clinical Relationships, and Transforming Care.
AI in healthcare is reshaping patient roles and clinician dynamics. Explore insights from our review of a pivotal PubMed article. 🤖📊
Sprinter Health Raises $55 Million to Enhance Home Healthcare Services
Sprinter Health secures $55 million to enhance in-home healthcare services, providing convenient patient care through technology. 🏥💰
Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users.
Exploring hyper-parameter tuning methods in machine learning for predicting high-cost healthcare users reveals AUC improvements from 0.82 to 0.84. 📈🤖