How to unlock useful data from EHRs using NLP

Huge amounts of medical records can be parsed using natural language processing to give payers and providers important information. Emtelligent's CEO and founder, Dr Tim O'Connell, talks about the strategy used by his business. There are various phases involved in applying Natural Language Processing (NLP) to extract meaningful data from Electronic Health Records (EHRs). The data is first preprocessed to manage missing data and remove unrelated information. Tokenization then divides the text into smaller chunks. Medical diseases, drugs, and patient demographics are just a few examples of the types of entities that named entity recognition (NER) recognizes and categorizes. While sentiment analysis extracts subjective information, connection extraction establishes the relationships between items. By transforming unstructured data into a structured manner, information extraction enables the examination of test findings, vital signs, and allergies. NLP can support healthcare decision-making and incorporate processed data.