袗胁褌芯褉写褘遥 卸邪褉懈褟谢邪薪褘屑写邪褉褘
Imanbek, B.
袘邪褉谢褘覜 卸邪褉懈褟谢邪薪褘屑写邪褉
24
袘械谢褋械薪写褨谢褨泻 卸褘谢写邪褉褘
6
袞邪褉懈褟谢邪薪褘屑 褌爷褉谢械褉褨
3
2026 (5)
- Architecting the Orthopedical Clinical AI Pipeline: A Review of Integrating Foundation Models and FHIR for Agentic Clinical Assistants and Digital Twins
- Explainable Machine Learning for Volatile Fatty Acid Soft-Sensing in Anaerobic Digestion: A Pilot Feasibility Study
- Non-Imaging Differential Diagnosis of Lower Limb Osteoarthritis: An Interpretable Machine Learning Framework
- Interpretable Machine Learning-Based Differential Diagnosis of Hip and Knee Osteoarthritis Using Routine Preoperative Clinical and Laboratory Data
- Benchmarking Tabular Foundation Models for Total Volatile Fatty Acid Prediction in Anaerobic Digestion
2025 (11)
- Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health
- Assessment of the Impact of Air Quality on Rehabilitation Effectiveness Using an IoMT-Based HRV Monitoring System
- IoT-Based Unsupervised Learning for Characterizing Laboratory Operational States to Improve Safety and Sustainability
- Neural Network-Based Analysis of Flame States in Pulverised Coal and Biomass Co-Combustion
- Interpretable Machine Learning for Coronary Artery Disease Risk Stratification: A SHAP-Based Analysis
- Enhancing Cardiovascular Disease Classification with Routine Blood Tests Using an Explainable AI Approach
- A Review of Innovative Medical Rehabilitation Systems with Scalable AI-Assisted Platforms for Sensor-Based Recovery Monitoring
- A Hybrid Machine Learning Approach for High-Accuracy Energy Consumption Prediction Using Indoor Environmental Quality Sensors
- A Wearable IoT-Based Measurement System for Real-Time Cardiovascular Risk Prediction Using Heart Rate Variability
- Explainable AI for Coronary Artery Disease Stratification Using Routine Clinical Data
- Analyzing the Impact of Air Quality on Cognitive Skills Using Machine Learning in a Smart Building Environment