Deep reinforced cognitive analytics algorithm (DRCAM): An advanced method to early detection of cognitive skill...

Area of Science:

• Artificial Intelligence in Healthcare
• Cognitive Science
• Machine Learning for Medical Diagnosis

Background:

• Cognitive impairments require accurate diagnosis and effective management strategies.
• Integrating diverse data sources can improve diagnostic precision.
• Current methods often lack comprehensive analysis of multimodal patient data.

Purpose of the Study:

• To propose a Deep Reinforced Cognitive Analytics Model (DRCAM) for enhanced diagnosis and management of cognitive impairments.
• To leverage multimodal learning and reinforcement-based interventions for improved patient care.
• To develop a novel approach for fusing neuroimaging, sensor, test, and text data.

Main Methods:

• Utilized Multimodal Transformers (MMT) for feature fusion from neuroimaging, wearable sensors, neuropsychological tests, and text.
• Employed a CNN-LSTM hybrid model for spatial and temporal dependency mapping.
• Integrated a Deep Q-Network (DQN) for cognitive training guidance and a Temporal Convolution Network (TCN) for long-term predictions.

Main Results:

• The Multimodal Transformers (MMT) model achieved a classification accuracy of 90-92%.
• The proposed DRCAM demonstrated superior performance compared to conventional cognitive assessment models.
• The model showed improved accuracy, efficacy in intervention, and potential for explainability.

Conclusions:

• The Deep Reinforced Cognitive Analytics Model (DRCAM) offers a significant advancement in cognitive impairment detection and management.
• Multimodal data integration and reinforcement learning provide a powerful framework for personalized cognitive interventions.
• The model's scalability and performance highlight its potential for diverse healthcare applications.