Kiruthika Subramani

In this session, I will explore the innovative integration of ML Model and the Gemini Large Language Model (LLM) to develop a cutting-edge predictive model aimed at enhancing pregnancy risk management. This model is designed to not only assess the severity of pregnancy-related risks but also to translate these findings into regional languages, thereby making healthcare services more accessible and accurate for diverse populations.

I will begin by providing a comprehensive overview of the model’s architecture, detailing how robust machine learning algorithms are combined with the advanced natural language processing capabilities of the Gemini LLM. This integration allows for precise risk assessment and effective communication of complex medical information in a way that is easily understandable by patients and healthcare providers alike.

Attendees will gain valuable insights into the practical application of this model in real-world scenarios. I will present case studies and examples demonstrating how the model has been used to predict and manage pregnancy risks, ultimately improving patient outcomes. Additionally, I will discuss the challenges faced during the development and deployment of the model, and how these were overcome.

Furthermore, I will highlight the benefits of combining machine learning with natural language processing in the healthcare sector. This includes improved accuracy in risk assessment, enhanced patient communication, and the potential for broader implementation across various healthcare settings. By the end of the session, attendees will have a thorough understanding of how this innovative approach can transform pregnancy risk management and contribute to better healthcare outcomes overall.