Introducing Batch Mode Processing for ICD-10-CM and RxNorm Ontology Linking
Launched at AWS re:Invent 2019, Amazon Comprehend Medical ICD-10-CM and RXNorm Ontology Linking extracts medical condition and medication entities from medical text and links them to the relevant ICD-10-CM and RXNorm concepts respectively. Medical ontologies, such as ICD-10, make it possible to classify unstructured medical information into standardized codes that downstream healthcare applications, such as revenue cycle management tools (medical coding) can read. Today we’re happy to announce that this API can now process batches of documents stored in an Amazon Simple Storage Service (S3).
Launched at AWS re:Invent 2019, Amazon Comprehend Medical ICD-10-CM and RXNorm Ontology Linking extracts medical condition and medication entities from medical text and links them to the relevant ICD-10-CM and RXNorm concepts respectively. Medical ontologies, such as ICD-10, make it possible to classify unstructured medical information into standardized codes that downstream healthcare applications, such as revenue cycle management tools (medical coding) can read. Today we’re happy to announce that this API can now process batches of documents stored in an Amazon Simple Storage Service (S3).
Using Batch Amazon Comprehend Medical ICD-10-CM and RXNorm Ontology Linking APIs, developers can quickly and accurately extract codes (e.g. “R51” as the ICD-10-CM code for headache) from a variety of data sources, such as doctor’s notes or patient health records in batch mode. Our deep learning approach to ontology linking provides much higher accuracy than existing rules-based systems by understanding the context each entity is found in.
Developers can use the linked ICD-10-CM and RXNorm concepts to build applications for use cases like revenue cycle management (medical coding) or population health management. ICD-10-CM and RXNorm Ontology Linking are available in batch mode, even at very large scale, no machine learning expertise required.