Talk · Procedia Computer Science · January 1, 2024

Integrating deep learning and reinforcement learning into a digital twin architecture for medical predictions.

Nursultan Alpysbay, Kateryna V. Kolesnikova, Tolganay Chinibayeva

Abstract

In the course of the research, a digital patient twin architecture based on deep reinforcement learning techniques was developed. The aim of the paper is to develop and validate a digital patient twin architecture that integrates deep learning and reinforcement learning techniques to improve the accuracy of medical predictions and individualize treatment. The existing information-analytical systems for health monitoring were analyzed, which allowed us to identify their limitations and define areas for improvement. The developed digital twin architecture integrates data from various sources including electronic health records, biometric sensors and genetic information. A deep reinforcement learning model is trained on patient data and tested to evaluate its ability to accurately predict health status. Results showed high accuracy, completeness, and F-measure scores, indicating the model's ability to effectively classify and predict health risks. The proposed system provides physicians with a tool for personalized diagnostics and development of individual treatment plans. The architecture of the digital twin of the patient is a promising solution for improving the accuracy of medical prognoses and optimizing treatment processes in clinical practice.

Machine Learning in HealthcareArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcaredeep reinforcement learningmedical predictionpersonalized treatment

Integrating deep learning and reinforcement learning into a digital twin architecture for medical predictions.

Abstract

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