Values.SNOMEDCTAttributeSourceThe extracted attributes that relate to an entity. An extracted segment of the text that is an attribute of an entity, or otherwise related to an entity, such as the dosage of a medication taken.
type nonrec t = {category : SNOMEDCTEntityCategory.t option;The category of the detected attribute. Possible categories include MEDICAL_CONDITION, ANATOMY, and TEST_TREATMENT_PROCEDURE.
*)type_ : SNOMEDCTAttributeType.t option;The type of attribute. Possible types include DX_NAME, ACUITY, DIRECTION, SYSTEM_ORGAN_SITE,TEST_NAME, TEST_VALUE, TEST_UNIT, PROCEDURE_NAME, and TREATMENT_NAME.
*)score : Float_.t option;The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
*)relationshipScore : Float_.t option;The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
*)relationshipType : SNOMEDCTRelationshipType.t option;The type of relationship that exists between the entity and the related attribute.
*)id : Integer.t option;The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)beginOffset : Integer.t option;The 0-based character offset in the input text that shows where the attribute begins. The offset returns the UTF-8 code point in the string.
*)endOffset : Integer.t option;The 0-based character offset in the input text that shows where the attribute ends. The offset returns the UTF-8 code point in the string.
*)text : String_.t option;The segment of input text extracted as this attribute.
*)traits : SNOMEDCTTraitList.t option;Contextual information for an attribute. Examples include signs, symptoms, diagnosis, and negation.
*)sNOMEDCTConcepts : SNOMEDCTConceptList.t option;The SNOMED-CT concepts specific to an attribute, along with a score indicating the likelihood of the match.
*)}