Changes This release supports new set of entities and traits. It also adds new category (BEHAVIORAL_ENVIRONMENTAL_SOCIAL).
Changes This release adds a new set of APIs (synchronous and batch) to support the SNOMED-CT ontology.
Changes The InferICD10CM API now returns TIME_EXPRESSION entities that refer to medical conditions.
Changes This release adds the relationships between MedicalCondition and Anatomy in DetectEntitiesV2 API.
Changes New Batch Ontology APIs for ICD-10 and RxNorm will provide batch capability of linking the information extracted by Comprehend Medical to medical ontologies. The new ontology linking APIs make it easy to detect medications and medical conditions in unstructured clinical text and link them to RxNorm and ICD-10-CM codes respectively. This new feature can help you reduce the cost, time and effort of processing large amounts of unstructured medical text with high accuracy.
Changes New Time Expression feature, part of DetectEntitiesV2 API will provide temporal relations to existing NERe entities such as Medication, Test, Treatment, Procedure and Medical conditions.
Changes New Ontology linking APIs will provides medication concepts normalization and Diagnoses codes from input text. In this release we will provide two APIs - RxNorm and ICD10-CM.
Changes Use Amazon Comprehend Medical to analyze medical text stored in the specified Amazon S3 bucket. Use the console to create and manage batch analysis jobs, or use the batch APIs to detect both medical entities and protected health information (PHI). The batch APIs start, stop, list, and retrieve information about batch analysis jobs. This release also includes DetectEntitiesV2 operation which returns the Acuity and Direction entities as attributes instead of types.
Changes The first release of Comprehend Medical includes two APIs, detectPHI and detectEntities. DetectPHI extracts PHI from your clinical text, and detectEntities extracts entities such as medication, medical conditions, or anatomy. DetectEntities also extracts attributes (e.g. dosage for medication) and identifies contextual traits (e.g. negation) for each entity.