infer_snomedct(**kwargs)¶InferSNOMEDCT detects possible medical concepts as entities and links them to codes from the Systematized Nomenclature of Medicine, Clinical Terms (SNOMED-CT) ontology
See also: AWS API Documentation
Request Syntax
response = client.infer_snomedct(
Text='string'
)
[REQUIRED]
The input text to be analyzed using InferSNOMEDCT. The text should be a string with 1 to 10000 characters.
{
'Entities': [
{
'Id': 123,
'Text': 'string',
'Category': 'MEDICAL_CONDITION'|'ANATOMY'|'TEST_TREATMENT_PROCEDURE',
'Type': 'DX_NAME'|'TEST_NAME'|'PROCEDURE_NAME'|'TREATMENT_NAME',
'Score': ...,
'BeginOffset': 123,
'EndOffset': 123,
'Attributes': [
{
'Category': 'MEDICAL_CONDITION'|'ANATOMY'|'TEST_TREATMENT_PROCEDURE',
'Type': 'ACUITY'|'QUALITY'|'DIRECTION'|'SYSTEM_ORGAN_SITE'|'TEST_VALUE'|'TEST_UNIT',
'Score': ...,
'RelationshipScore': ...,
'RelationshipType': 'ACUITY'|'QUALITY'|'TEST_VALUE'|'TEST_UNITS'|'DIRECTION'|'SYSTEM_ORGAN_SITE',
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Text': 'string',
'Traits': [
{
'Name': 'NEGATION'|'DIAGNOSIS'|'SIGN'|'SYMPTOM'|'PERTAINS_TO_FAMILY'|'HYPOTHETICAL'|'LOW_CONFIDENCE'|'PAST_HISTORY'|'FUTURE',
'Score': ...
},
],
'SNOMEDCTConcepts': [
{
'Description': 'string',
'Code': 'string',
'Score': ...
},
]
},
],
'Traits': [
{
'Name': 'NEGATION'|'DIAGNOSIS'|'SIGN'|'SYMPTOM'|'PERTAINS_TO_FAMILY'|'HYPOTHETICAL'|'LOW_CONFIDENCE'|'PAST_HISTORY'|'FUTURE',
'Score': ...
},
],
'SNOMEDCTConcepts': [
{
'Description': 'string',
'Code': 'string',
'Score': ...
},
]
},
],
'PaginationToken': 'string',
'ModelVersion': 'string',
'SNOMEDCTDetails': {
'Edition': 'string',
'Language': 'string',
'VersionDate': 'string'
},
'Characters': {
'OriginalTextCharacters': 123
}
}
Response Structure
The collection of medical concept entities extracted from the input text and their associated information. For each entity, the response provides the entity text, the entity category, where the entity text begins and ends, and the level of confidence that Comprehend Medical has in the detection and analysis. Attributes and traits of the entity are also returned.
The collection of medical entities extracted from the input text and their associated information. For each entity, the response provides the entity text, the entity category, where the entity text begins and ends, and the level of confidence that Comprehend Medical has in the detection and analysis. Attributes and traits of the entity are also returned.
The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
The segment of input text extracted as this entity.
The category of the detected entity. Possible categories are MEDICAL_CONDITION, ANATOMY, or TEST_TREATMENT_PROCEDURE.
Describes the specific type of entity with category of entities. Possible types include DX_NAME, ACUITY, DIRECTION, SYSTEM_ORGAN_SITE, TEST_NAME, TEST_VALUE, TEST_UNIT, PROCEDURE_NAME, or TREATMENT_NAME.
The level of confidence that Comprehend Medical has in the accuracy of the detected entity.
The 0-based character offset in the input text that shows where the entity begins. The offset returns the UTF-8 code point in the string.
The 0-based character offset in the input text that shows where the entity ends. The offset returns the UTF-8 code point in the string.
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.
The 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.
The category of the detected attribute. Possible categories include MEDICAL_CONDITION, ANATOMY, and TEST_TREATMENT_PROCEDURE.
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.
The level of confidence that Comprehend Medical has that the segment of text is correctly recognized as an attribute.
The level of confidence that Comprehend Medical has that this attribute is correctly related to this entity.
The type of relationship that exists between the entity and the related attribute.
The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
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.
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.
The segment of input text extracted as this attribute.
Contextual information for an attribute. Examples include signs, symptoms, diagnosis, and negation.
Contextual information for an entity.
The name or contextual description of a detected trait.
The level of confidence that Comprehend Medical has in the accuracy of a detected trait.
The SNOMED-CT concepts specific to an attribute, along with a score indicating the likelihood of the match.
The SNOMED-CT concepts that the entity could refer to, along with a score indicating the likelihood of the match.
The description of the SNOMED-CT concept.
The numeric ID for the SNOMED-CT concept.
The level of confidence Comprehend Medical has that the entity should be linked to the identified SNOMED-CT concept.
Contextual information for the entity.
Contextual information for an entity.
The name or contextual description of a detected trait.
The level of confidence that Comprehend Medical has in the accuracy of a detected trait.
The SNOMED concepts that the entity could refer to, along with a score indicating the likelihood of the match.
The SNOMED-CT concepts that the entity could refer to, along with a score indicating the likelihood of the match.
The description of the SNOMED-CT concept.
The numeric ID for the SNOMED-CT concept.
The level of confidence Comprehend Medical has that the entity should be linked to the identified SNOMED-CT concept.
If the result of the request is truncated, the pagination token can be used to fetch the next page of entities.
The version of the model used to analyze the documents, in the format n.n.n You can use this information to track the model used for a particular batch of documents.
The details of the SNOMED-CT revision, including the edition, language, and version date.
The edition of SNOMED-CT used. The edition used for the InferSNOMEDCT editions is the US edition.
The language used in the SNOMED-CT ontology. All Amazon Comprehend Medical operations are US English (en).
The version date of the SNOMED-CT ontology used.
The number of characters in the input request documentation.
The number of characters present in the input text document as processed by Comprehend Medical.
Exceptions
ComprehendMedical.Client.exceptions.InternalServerExceptionComprehendMedical.Client.exceptions.ServiceUnavailableExceptionComprehendMedical.Client.exceptions.TooManyRequestsExceptionComprehendMedical.Client.exceptions.InvalidRequestExceptionComprehendMedical.Client.exceptions.InvalidEncodingExceptionComprehendMedical.Client.exceptions.TextSizeLimitExceededException