Healthcare Personalized System Recommendation Using Processing of Natural Language in Wearable Data

The goal of the healthcare personalized system recommendation is to improve patient management by using sensor data and natural language processing (NLP). The application of NLP enables the analysis of wearable data in a manner that health recommendations can be made. Struggling with contextual understanding, low adaptability across various health conditions, and limited data processing efficiency are problems with current practices. This paper seeks to solve these limitations by introducing a Transformer-Based NLP Model (T-NLPM) for Personalized Health Insights, which employs deep learning to process recommendations derived from wearable sensor data. The proposed framework interprets physiological signals, textbook health records, and patient feedback and uses these to enhance the effectiveness of real-time systems. This is important in continuous health evaluation, early disease detection, and delivering customized treatments. In this paper, experimental results illustrate that T-NLPM surpasses other models in accuracy of recommendation, improves recommendation adaptability, and increases performance by providing less false alerts at critical periods for better patient health management. The proposed method achieves the health monitoring by 97.80%, early disease prediction by 98.16%, Adaptability by 98.36%.