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Accuracy in Medical NLP: It’s Never Just a Single Number

Five Important Criteria for Assessing the Quality and Performance

of Medical Language Understanding


WHAT IS MEDICAL NATURAL LANGUAGE PROCESSING (NLP)?



Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables machine understanding of human language. In recent years, NLP has become part of consumer products and performs with a sometimes-surprising degree of accuracy. Virtual assistants like Apple’s Siri, Google Assistant and Amazon’s Alexa use NLP to understand spoken questions like “what’s the weather like today?”. They are able to understand the meaning of the question, and then use data from online sources to reply with a meaningful response.


Medical care providers produce voluminous documents – necessary for recording procedures, diagnoses, and patient insights – in the form of written prose. This ‘unstructured data’ is commonly estimated to comprise nearly 80% of medical data, but because of its unstructured nature, is only usable by humans reading the documents.

Medical care providers produce voluminous documents – necessary for recording procedures, diagnoses, and patient insights – in the form of written prose. This ‘unstructured data’ is estimated to comprise nearly 80% of medical data, but because of its unstructured nature, is only usable by humans reading the documents. This means that when an unconscious patient comes into the emergency department, after ensuring the patient is stable, one of the first things the emergency doctor will likely do is take several minutes to read through the patient’s chart. Wouldn’t it be more effective if NLP software had read and understood the patient’s chart and could present a relevant history to the care provider that they could read in 30 seconds? Or read aloud a summary to the provider when they ask their smart device, “Assistant, tell me about Mrs. Zhang?”.





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