Cardiovascular Disease Prediction Using Langchain
K Ananthajothi, Joshua A. David, A Kavin
Abstract
While traditional methods struggle to capture the full picture from electronic health records (EHRs), LangChain, a deep learning architecture, emerges as a promising tool for cardiovascular disease (CVD) prediction. By seamlessly integrating natural language processing (NLP) and traditional machine learning, LangChain delves into the rich tapestry of clinical notes, extracting crucial insights often hidden within the text. This, combined with its ability to handle diverse data types and capture temporal relationships, allows LangChain to create a comprehensive understanding of individual health profiles. This not only translates to improved CVD prediction accuracy but also unlocks valuable insights into the disease’ s progression, empowering healthcare professionals with the knowledge to implement early interventions and ultimately improve patient care and outcomes.