2020/06/30 - AWS Comprehend Medical - 3 updated api methods
Changes This release adds the relationships between MedicalCondition and Anatomy in DetectEntitiesV2 API.
{'Entities': {'Attributes': {'RelationshipType': {'SYSTEM_ORGAN_SITE'}}},
'UnmappedAttributes': {'Attribute': {'RelationshipType': {'SYSTEM_ORGAN_SITE'}}}}
The DetectEntities operation is deprecated. You should use the DetectEntitiesV2 operation instead.
Inspects the clinical text for a variety of medical entities and returns specific information about them such as entity category, location, and confidence score on that information .
!DANGER!
This operation is deprecated and may not function as expected. This operation should not be used going forward and is only kept for the purpose of backwards compatiblity.
See also: AWS API Documentation
Request Syntax
client.detect_entities(
Text='string'
)
string
[REQUIRED]
A UTF-8 text string containing the clinical content being examined for entities. Each string must contain fewer than 20,000 bytes of characters.
dict
Response Syntax
{
'Entities': [
{
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Score': ...,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
],
'Attributes': [
{
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Score': ...,
'RelationshipScore': ...,
'RelationshipType': 'EVERY'|'WITH_DOSAGE'|'ADMINISTERED_VIA'|'FOR'|'NEGATIVE'|'OVERLAP'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_VALUE'|'TEST_UNITS'|'DIRECTION'|'SYSTEM_ORGAN_SITE',
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
]
},
]
},
],
'UnmappedAttributes': [
{
'Type': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Attribute': {
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Score': ...,
'RelationshipScore': ...,
'RelationshipType': 'EVERY'|'WITH_DOSAGE'|'ADMINISTERED_VIA'|'FOR'|'NEGATIVE'|'OVERLAP'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_VALUE'|'TEST_UNITS'|'DIRECTION'|'SYSTEM_ORGAN_SITE',
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
]
}
},
],
'PaginationToken': 'string',
'ModelVersion': 'string'
}
Response Structure
(dict) --
Entities (list) --
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 Amazon Comprehend Medical has in the detection and analysis. Attributes and traits of the entity are also returned.
(dict) --
Provides information about an extracted medical entity.
Id (integer) --
The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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.
EndOffset (integer) --
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.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of the detection.
Text (string) --
The segment of input text extracted as this entity.
Category (string) --
The category of the entity.
Type (string) --
Describes the specific type of entity with category of entities.
Traits (list) --
Contextual information for the entity.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
Attributes (list) --
The extracted attributes that relate to this entity.
(dict) --
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. It contains information about the attribute such as id, begin and end offset within the input text, and the segment of the input text.
Type (string) --
The type of attribute.
Score (float) --
The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
RelationshipScore (float) --
The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
RelationshipType (string) --
The type of relationship between the entity and attribute. Type for the relationship is OVERLAP , indicating that the entity occurred at the same time as the Date_Expression .
Id (integer) --
The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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) --
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) --
The segment of input text extracted as this attribute.
Category (string) --
The category of attribute.
Traits (list) --
Contextual information for this attribute.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
UnmappedAttributes (list) --
Attributes extracted from the input text that we were unable to relate to an entity.
(dict) --
An attribute that we extracted, but were unable to relate to an entity.
Type (string) --
The type of the attribute, could be one of the following values: "MEDICATION", "MEDICAL_CONDITION", "ANATOMY", "TEST_AND_TREATMENT_PROCEDURE" or "PROTECTED_HEALTH_INFORMATION".
Attribute (dict) --
The specific attribute that has been extracted but not mapped to an entity.
Type (string) --
The type of attribute.
Score (float) --
The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
RelationshipScore (float) --
The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
RelationshipType (string) --
The type of relationship between the entity and attribute. Type for the relationship is OVERLAP , indicating that the entity occurred at the same time as the Date_Expression .
Id (integer) --
The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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) --
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) --
The segment of input text extracted as this attribute.
Category (string) --
The category of attribute.
Traits (list) --
Contextual information for this attribute.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
PaginationToken (string) --
If the result of the previous request to DetectEntities was truncated, include the PaginationToken to fetch the next page of entities.
ModelVersion (string) --
The version of the model used to analyze the documents. The version number looks like X.X.X. You can use this information to track the model used for a particular batch of documents.
{'Entities': {'Attributes': {'RelationshipType': {'SYSTEM_ORGAN_SITE'}}},
'UnmappedAttributes': {'Attribute': {'RelationshipType': {'SYSTEM_ORGAN_SITE'}}}}
Inspects the clinical text for a variety of medical entities and returns specific information about them such as entity category, location, and confidence score on that information. Amazon Comprehend Medical only detects medical entities in English language texts.
The DetectEntitiesV2 operation replaces the DetectEntities operation. This new action uses a different model for determining the entities in your medical text and changes the way that some entities are returned in the output. You should use the DetectEntitiesV2 operation in all new applications.
The DetectEntitiesV2 operation returns the Acuity and Direction entities as attributes instead of types.
See also: AWS API Documentation
Request Syntax
client.detect_entities_v2(
Text='string'
)
string
[REQUIRED]
A UTF-8 string containing the clinical content being examined for entities. Each string must contain fewer than 20,000 bytes of characters.
dict
Response Syntax
{
'Entities': [
{
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Score': ...,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
],
'Attributes': [
{
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Score': ...,
'RelationshipScore': ...,
'RelationshipType': 'EVERY'|'WITH_DOSAGE'|'ADMINISTERED_VIA'|'FOR'|'NEGATIVE'|'OVERLAP'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_VALUE'|'TEST_UNITS'|'DIRECTION'|'SYSTEM_ORGAN_SITE',
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
]
},
]
},
],
'UnmappedAttributes': [
{
'Type': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Attribute': {
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Score': ...,
'RelationshipScore': ...,
'RelationshipType': 'EVERY'|'WITH_DOSAGE'|'ADMINISTERED_VIA'|'FOR'|'NEGATIVE'|'OVERLAP'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_VALUE'|'TEST_UNITS'|'DIRECTION'|'SYSTEM_ORGAN_SITE',
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
]
}
},
],
'PaginationToken': 'string',
'ModelVersion': 'string'
}
Response Structure
(dict) --
Entities (list) --
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 in the detection and analysis. Attributes and traits of the entity are also returned.
(dict) --
Provides information about an extracted medical entity.
Id (integer) --
The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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.
EndOffset (integer) --
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.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of the detection.
Text (string) --
The segment of input text extracted as this entity.
Category (string) --
The category of the entity.
Type (string) --
Describes the specific type of entity with category of entities.
Traits (list) --
Contextual information for the entity.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
Attributes (list) --
The extracted attributes that relate to this entity.
(dict) --
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. It contains information about the attribute such as id, begin and end offset within the input text, and the segment of the input text.
Type (string) --
The type of attribute.
Score (float) --
The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
RelationshipScore (float) --
The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
RelationshipType (string) --
The type of relationship between the entity and attribute. Type for the relationship is OVERLAP , indicating that the entity occurred at the same time as the Date_Expression .
Id (integer) --
The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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) --
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) --
The segment of input text extracted as this attribute.
Category (string) --
The category of attribute.
Traits (list) --
Contextual information for this attribute.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
UnmappedAttributes (list) --
Attributes extracted from the input text that couldn't be related to an entity.
(dict) --
An attribute that we extracted, but were unable to relate to an entity.
Type (string) --
The type of the attribute, could be one of the following values: "MEDICATION", "MEDICAL_CONDITION", "ANATOMY", "TEST_AND_TREATMENT_PROCEDURE" or "PROTECTED_HEALTH_INFORMATION".
Attribute (dict) --
The specific attribute that has been extracted but not mapped to an entity.
Type (string) --
The type of attribute.
Score (float) --
The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
RelationshipScore (float) --
The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
RelationshipType (string) --
The type of relationship between the entity and attribute. Type for the relationship is OVERLAP , indicating that the entity occurred at the same time as the Date_Expression .
Id (integer) --
The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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) --
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) --
The segment of input text extracted as this attribute.
Category (string) --
The category of attribute.
Traits (list) --
Contextual information for this attribute.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
PaginationToken (string) --
If the result to the DetectEntitiesV2 operation was truncated, include the PaginationToken to fetch the next page of entities.
ModelVersion (string) --
The version of the model used to analyze the documents. The version number looks like X.X.X. You can use this information to track the model used for a particular batch of documents.
{'Entities': {'Attributes': {'RelationshipType': {'SYSTEM_ORGAN_SITE'}}}}
Inspects the clinical text for protected health information (PHI) entities and returns the entity category, location, and confidence score for each entity. Amazon Comprehend Medical only detects entities in English language texts.
See also: AWS API Documentation
Request Syntax
client.detect_phi(
Text='string'
)
string
[REQUIRED]
A UTF-8 text string containing the clinical content being examined for PHI entities. Each string must contain fewer than 20,000 bytes of characters.
dict
Response Syntax
{
'Entities': [
{
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Score': ...,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
],
'Attributes': [
{
'Type': 'NAME'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'GENERIC_NAME'|'BRAND_NAME'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_NAME'|'TEST_VALUE'|'TEST_UNITS'|'PROCEDURE_NAME'|'TREATMENT_NAME'|'DATE'|'AGE'|'CONTACT_POINT'|'EMAIL'|'IDENTIFIER'|'URL'|'ADDRESS'|'PROFESSION'|'SYSTEM_ORGAN_SITE'|'DIRECTION'|'QUALITY'|'QUANTITY'|'TIME_EXPRESSION'|'TIME_TO_MEDICATION_NAME'|'TIME_TO_DX_NAME'|'TIME_TO_TEST_NAME'|'TIME_TO_PROCEDURE_NAME'|'TIME_TO_TREATMENT_NAME',
'Score': ...,
'RelationshipScore': ...,
'RelationshipType': 'EVERY'|'WITH_DOSAGE'|'ADMINISTERED_VIA'|'FOR'|'NEGATIVE'|'OVERLAP'|'DOSAGE'|'ROUTE_OR_MODE'|'FORM'|'FREQUENCY'|'DURATION'|'STRENGTH'|'RATE'|'ACUITY'|'TEST_VALUE'|'TEST_UNITS'|'DIRECTION'|'SYSTEM_ORGAN_SITE',
'Id': 123,
'BeginOffset': 123,
'EndOffset': 123,
'Text': 'string',
'Category': 'MEDICATION'|'MEDICAL_CONDITION'|'PROTECTED_HEALTH_INFORMATION'|'TEST_TREATMENT_PROCEDURE'|'ANATOMY'|'TIME_EXPRESSION',
'Traits': [
{
'Name': 'SIGN'|'SYMPTOM'|'DIAGNOSIS'|'NEGATION',
'Score': ...
},
]
},
]
},
],
'PaginationToken': 'string',
'ModelVersion': 'string'
}
Response Structure
(dict) --
Entities (list) --
The collection of PHI 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 Amazon Comprehend Medical has in its detection.
(dict) --
Provides information about an extracted medical entity.
Id (integer) --
The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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.
EndOffset (integer) --
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.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of the detection.
Text (string) --
The segment of input text extracted as this entity.
Category (string) --
The category of the entity.
Type (string) --
Describes the specific type of entity with category of entities.
Traits (list) --
Contextual information for the entity.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
Attributes (list) --
The extracted attributes that relate to this entity.
(dict) --
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. It contains information about the attribute such as id, begin and end offset within the input text, and the segment of the input text.
Type (string) --
The type of attribute.
Score (float) --
The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
RelationshipScore (float) --
The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
RelationshipType (string) --
The type of relationship between the entity and attribute. Type for the relationship is OVERLAP , indicating that the entity occurred at the same time as the Date_Expression .
Id (integer) --
The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
BeginOffset (integer) --
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) --
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) --
The segment of input text extracted as this attribute.
Category (string) --
The category of attribute.
Traits (list) --
Contextual information for this attribute.
(dict) --
Provides contextual information about the extracted entity.
Name (string) --
Provides a name or contextual description about the trait.
Score (float) --
The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
PaginationToken (string) --
If the result of the previous request to DetectPHI was truncated, include the PaginationToken to fetch the next page of PHI entities.
ModelVersion (string) --
The version of the model used to analyze the documents. The version number looks like X.X.X. You can use this information to track the model used for a particular batch of documents.