Mining medical concepts from written clinical text, such as patient encounters, plays an important role in clinical analytics and decision-making applications, such as population analytics for providers, pre-authorization for payers, and adverse-event detection for pharma companies. Medical concepts contain medical conditions, medications, procedures, and other clinical events. Extracting medical concepts is a complicated process due to the specialist knowledge required and the broad use of synonyms in the medical field. Furthermore, to make detected concepts useful for large-scale analytics and decision-making applications, they have to be codified. This is a process where a specialist looks up matching codes from a medical ontology, often containing tens to hundreds of thousands of concepts.
To solve these problems, Amazon Comprehend Medical provides a fast and accurate way to automatically extract medical concepts from the written text found in clinical documents. You can now also use a new feature to automatically standardize and link

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