Clinical and research applications of natural language processing for heart failure.

Abstract

Natural language processing (NLP) is a burgeoning field of machine learning/artificial intelligence that focuses on the computational processing of human language. Researchers and clinicians are using NLP methods to advance the field of medicine in general and in heart failure (HF), in particular, by processing vast amounts of previously untapped semi-structured and unstructured textual data in electronic health records. NLP has several applications to clinical research, including dramatically improving processes for cohort assembly, disease phenotyping, and outcome ascertainment, among others. NLP also has the potential to improve direct clinical care through early detection, accurate diagnosis, and evidence-based management of patients with HF. In this state-of-the-art review, we present a general overview of NLP methods and review clinical and research applications in the field of HF. We also propose several potential future directions of this emerging and rapidly evolving technological breakthrough.