Hybrid Intelligence for Healthcare: Transforming Tomorrow’s Mental Health Diagnosis with Multi-Model Architecture
Keywords:
Disease Prediction, Logistic Regression, Transformer Model, LLaMA 3, Mental Health, Hybrid Model, Healthcare ChatbotAbstract
These research studies are focused on the applicability of traditional machine learning models such as logistic regression to advanced transformer models such as LLaMA 3 to enhance the prediction of diseases through easier access to healthcare services. Trained with artificial healthcare data related to mental health conditions, the hybrid model was better in accuracy and user engagement than standalone models. The system would allow for real-time user-friendly health insights through a chatbot interface owing to the structured prediction logistic regression capabilities and conversational power of the transformer. It would bring complex medical data and a patient in closer proximity, perhaps even in telemedicine or remote health monitoring. The hybrid model studied here shows the transformative role AI can play in healthcare by both predictive accuracy and user experience and suggests that such hybrid models should democratize access to healthcare for patients and enhance the provider's decision-making.
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References
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