Natural Language Processing for Diagnosis and Risk Assessment of Cardiovascular Disease

Even with better tools for diagnosis and prevention, heart disease continues to be a major killer. Diagnosis and prevention of heart disease require detection and awareness of risk factors. Disease progression modeling and clinical decision-making can both benefit from the automatic identification of these risk markers from clinical records. Many studies have tried, without success, to uncover the characteristics that put people at risk for cardiovascular disease. Many hybrid systems have been suggested that attempt to integrate knowledge-driven and data-driven approaches, however due to the usage of Machine Learning, rules, and dictionaries, methods, they frequently require substantial human input. Natural language processing and deep learning methods can be used to extract useful information from clinical tales. By using state-of-the-art methods involving stacked word embeddings to the 2014 i2b2 challenge, this article hopes to contribute to the state of the art in this area. The suggested model makes use of a stacking method that combines different embeddings (CHARACTER-BERT Embedding) to better understand the data.The application of this method to the i2b2 cardiovascular risk factors challenges dataset resulted in significant enhancement of our model’s performance. An F1 score of 92.65% is an excellent result. When compared to competing models and systems created for the 2014 i2b2 challenge, our proposed model fares exceptionally well.