Machine Learning-Based Approach for Identifying Research Gaps: COVID-19 as a Case Study.
A machine learning approach identifies COVID-19 research gaps, highlighting six key topics for future exploration. 📊🔍
Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.
Recent research enhances RWMA detection using machine learning and multi-view echocardiography. Promising results for early MI diagnosis! 🫀📊
Supporting Trustworthy AI Through Machine Unlearning.
Machine unlearning can enhance trustworthy AI by supporting ethical principles. However, it poses ethical risks that need addressing. 🤖📜
Fall risk prediction using temporal gait features and machine learning approaches.
AI enhances fall risk prediction through gait analysis. 🤖🚶♂️ Promising results with 96% accuracy using machine learning techniques. 📊
Adult co-creators’ emotional and psychological experiences of the co-creation process: a Health CASCADE scoping review protocol.
Exploring adult co-creators' emotional and psychological experiences in health co-creation processes. 🧠🤝 #HealthCASCADE #CoCreation
Comparing Cadence vs. Machine Learning Based Physical Activity Intensity Classifications: Variations in the Associations of Physical Activity With Mortality.
New research compares cadence and machine learning in classifying physical activity intensity and its impact on mortality. 📊🏃♂️
Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings.
Exploring the Frailty Care Bundle's impact on older patients' mobility using machine learning. 🤖👵 #Healthcare #FrailtyCare
Analysis of Responses of GPT-4 V to the Japanese National Clinical Engineer Licensing Examination.
GPT-4 V scored 86% on Japan's Clinical Engineer Exam. 📊 Performance varied by subject; caution advised in usage. ⚠️