Retracted: Medical Data Feature Learning Based on Probability and Depth Learning Mining: Model Development and...

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Big Data Analytics

Background:

• Big data technology offers advanced capabilities for medical data management and analysis.
• Deep learning and machine learning enhance healthcare by assisting diagnosis, personalizing treatment, and enabling intelligent processes.

Purpose of the Study:

• To analyze healthcare big data and develop an intelligent application for predicting hospital outpatient visits.
• To design a data feature learning model for effective patient treatment and optimal resource utilization.

Main Methods:

• Implemented a cascaded depth learning framework for feature transformation, selection, and classification.
• Developed a medical data feature learning model using probabilistic and deep learning for multimodal data analysis and disease risk assessment.

Main Results:

• The developed depth model accurately forecasts daily outpatient volumes, outperforming weekly or monthly predictions.
• Daily volume forecasting benefits from larger datasets and avoids error accumulation inherent in longer-term predictions.

Conclusions:

• Proposed data feature learning models effectively extract relationships in outpatient volume data for precise predictions.
• Accurate outpatient volume predictions facilitate rational medical resource allocation and advance intelligent healthcare.