The identifier that Comprehend Medical; generated for the job. The StartEntitiesDetectionV2Job operation returns this identifier in its response.

An object that contains the properties associated with a detection job.

The response's http status code.

The identifier that Amazon Comprehend Medical generated for the job. The StartICD10CMInferenceJob operation returns this identifier in its response.

An object that contains the properties associated with a detection job.

The response's http status code.

The identifier that Comprehend Medical; generated for the job. The StartPHIDetectionJob operation returns this identifier in its response.

An object that contains the properties associated with a detection job.

The response's http status code.

The identifier that Amazon Comprehend Medical generated for the job. The StartRxNormInferenceJob operation returns this identifier in its response.

An object that contains the properties associated with a detection job.

The response's http status code.

The identifier that Amazon Comprehend Medical generated for the job. The StartSNOMEDCTInferenceJob operation returns this identifier in its response.

Undocumented member.

The response's http status code.

A UTF-8 string containing the clinical content being examined for entities. Each string must contain fewer than 20,000 bytes of characters.

If the result to the DetectEntitiesV2 operation was truncated, include the PaginationToken to fetch the next page of entities.

Attributes extracted from the input text that couldn't be related to an entity.

The response's http status code.

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.

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.

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.

If the result of the previous request to DetectPHI was truncated, include the PaginationToken to fetch the next page of PHI entities.

The response's http status code.

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 Comprehend Medical; has in its detection.

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.

The input text used for analysis. The input for InferICD10CM is a string from 1 to 10000 characters.

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.

If the result of the previous request to InferICD10CM was truncated, include the PaginationToken to fetch the next page of medical condition entities.

The response's http status code.

The medical conditions detected in the text linked to ICD-10-CM concepts. If the action is successful, the service sends back an HTTP 200 response, as well as the entities detected.

The input text used for analysis. The input for InferRxNorm is a string from 1 to 10000 characters.

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.

If the result of the previous request to InferRxNorm was truncated, include the PaginationToken to fetch the next page of medication entities.

The response's http status code.

The medication entities detected in the text linked to RxNorm concepts. If the action is successful, the service sends back an HTTP 200 response, as well as the entities detected.

The input text to be analyzed using InferSNOMEDCT. The text should be a string with 1 to 10000 characters.

The number of characters in the input request documentation.

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.

If the result of the request is truncated, the pagination token can be used to fetch the next page of entities.

The details of the SNOMED-CT revision, including the edition, language, and version date.

The response's http status code.

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.

Filters the jobs that are returned. You can filter jobs based on their names, status, or the date and time that they were submitted. You can only set one filter at a time.

The maximum number of results to return in each page. The default is 100.

Identifies the next page of results to return.

A list containing the properties of each job returned.

Identifies the next page of results to return.

The response's http status code.

Filters the jobs that are returned. You can filter jobs based on their names, status, or the date and time that they were submitted. You can only set one filter at a time.

The maximum number of results to return in each page. The default is 100.

Identifies the next page of results to return.

A list containing the properties of each job that is returned.

Identifies the next page of results to return.

The response's http status code.

Filters the jobs that are returned. You can filter jobs based on their names, status, or the date and time that they were submitted. You can only set one filter at a time.

The maximum number of results to return in each page. The default is 100.

Identifies the next page of results to return.

A list containing the properties of each job returned.

Identifies the next page of results to return.

The response's http status code.

Filters the jobs that are returned. You can filter jobs based on their names, status, or the date and time that they were submitted. You can only set one filter at a time.

Identifies the next page of results to return.

Identifies the next page of results to return.

The maximum number of results to return in each page. The default is 100.

Identifies the next page of results to return.

The response's http status code.

Undocumented member.

The maximum number of results to return in each page. The default is 100.

Identifies the next page of InferSNOMEDCT results to return.

A list containing the properties of each job that is returned.

Identifies the next page of results to return.

The response's http status code.

A unique identifier for the request. If you don't set the client request token, Comprehend Medical; generates one for you.

The identifier of the job.

An AWS Key Management Service key to encrypt your output files. If you do not specify a key, the files are written in plain text.

The input configuration that specifies the format and location of the input data for the job.

The output configuration that specifies where to send the output files.

The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that grants Comprehend Medical; read access to your input data. For more information, see Role-Based Permissions Required for Asynchronous Operations.

The language of the input documents. All documents must be in the same language. Comprehend Medical; processes files in US English (en).

The identifier generated for the job. To get the status of a job, use this identifier with the DescribeEntitiesDetectionV2Job operation.

The response's http status code.

A unique identifier for the request. If you don't set the client request token, Comprehend Medical; generates one.

The identifier of the job.

An AWS Key Management Service key to encrypt your output files. If you do not specify a key, the files are written in plain text.

Specifies the format and location of the input data for the job.

Specifies where to send the output files.

The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that grants Comprehend Medical; read access to your input data. For more information, see Role-Based Permissions Required for Asynchronous Operations.

The language of the input documents. All documents must be in the same language.

The identifier generated for the job. To get the status of a job, use this identifier with the StartICD10CMInferenceJob operation.

The response's http status code.

A unique identifier for the request. If you don't set the client request token, Comprehend Medical; generates one.

The identifier of the job.

An AWS Key Management Service key to encrypt your output files. If you do not specify a key, the files are written in plain text.

Specifies the format and location of the input data for the job.

Specifies where to send the output files.

The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that grants Comprehend Medical; read access to your input data. For more information, see Role-Based Permissions Required for Asynchronous Operations.

The language of the input documents. All documents must be in the same language.

The identifier generated for the job. To get the status of a job, use this identifier with the DescribePHIDetectionJob operation.

The response's http status code.

A unique identifier for the request. If you don't set the client request token, Comprehend Medical; generates one.

The identifier of the job.

An AWS Key Management Service key to encrypt your output files. If you do not specify a key, the files are written in plain text.

Specifies the format and location of the input data for the job.

Specifies where to send the output files.

The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that grants Comprehend Medical; read access to your input data. For more information, see Role-Based Permissions Required for Asynchronous Operations.

The language of the input documents. All documents must be in the same language.

The identifier of the job.

The response's http status code.

A unique identifier for the request. If you don't set the client request token, Amazon Comprehend Medical generates one.

The user generated name the asynchronous InferSNOMEDCT job.

An AWS Key Management Service key used to encrypt your output files. If you do not specify a key, the files are written in plain text.

Undocumented member.

Undocumented member.

The Amazon Resource Name (ARN) of the AWS Identity and Access Management (IAM) role that grants Amazon Comprehend Medical read access to your input data.

The language of the input documents. All documents must be in the same language.

The identifier generated for the job. To get the status of a job, use this identifier with the StartSNOMEDCTInferenceJob operation.

The response's http status code.

The identifier of the medical entities job to stop.

The identifier of the medical entities detection job that was stopped.

The response's http status code.

The identifier of the job.

The identifier generated for the job. To get the status of job, use this identifier with the DescribeICD10CMInferenceJob operation.

The response's http status code.

The identifier of the PHI detection job to stop.

The identifier of the PHI detection job that was stopped.

The response's http status code.

The identifier of the job.

The identifier generated for the job. To get the status of job, use this identifier with the DescribeRxNormInferenceJob operation.

The response's http status code.

The job id of the asynchronous InferSNOMEDCT job to be stopped.

The identifier generated for the job. To get the status of job, use this identifier with the DescribeSNOMEDCTInferenceJob operation.

The response's http status code.

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 category of attribute.

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 numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.

The level of confidence that Comprehend Medical; has that this attribute is correctly related to this entity.

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.

The level of confidence that Comprehend Medical; has that the segment of text is correctly recognized as an attribute.

The segment of input text extracted as this attribute.

Contextual information for this attribute.

The type of attribute.

The number of characters present in the input text document as processed by Comprehend Medical.

Filters on the name of the job.

Filters the list of jobs based on job status. Returns only jobs with the specified status.

Filters the list of jobs based on the time that the job was submitted for processing. Returns only jobs submitted after the specified time. Jobs are returned in descending order, newest to oldest.

Filters the list of jobs based on the time that the job was submitted for processing. Returns only jobs submitted before the specified time. Jobs are returned in ascending order, oldest to newest.

The Amazon Resource Name (ARN) that gives Comprehend Medical; read access to your input data.

The time that the detection job completed.

The date and time that job metadata is deleted from the server. Output files in your S3 bucket will not be deleted. After the metadata is deleted, the job will no longer appear in the results of the ListEntitiesDetectionV2Job or the ListPHIDetectionJobs operation.

The input data configuration that you supplied when you created the detection job.

The identifier assigned to the detection job.

The name that you assigned to the detection job.

The current status of the detection job. If the status is FAILED, the Message field shows the reason for the failure.

The AWS Key Management Service key, if any, used to encrypt the output files.

The language code of the input documents.

The path to the file that describes the results of a batch job.

A description of the status of a job.

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.

The output data configuration that you supplied when you created the detection job.

The time that the detection job was submitted for processing.

The extracted attributes that relate to this 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 category of the entity.

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.

The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.

The level of confidence that Comprehend Medical; has in the accuracy of the detection.

The segment of input text extracted as this entity.

Contextual information for the entity.

Describes the specific type of entity with category of entities.

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 category of attribute. Can be either of DX_NAME or TIME_EXPRESSION.

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 numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.

The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.

The type of relationship between the entity and attribute. Type for the relationship can be either of OVERLAP or SYSTEM_ORGAN_SITE.

The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.

The segment of input text which contains the detected attribute.

The contextual information for the attribute. The traits recognized by InferICD10CM are DIAGNOSIS, SIGN, SYMPTOM, and NEGATION.

The type of attribute. InferICD10CM detects entities of the type DX_NAME.

The ICD-10-CM code that identifies the concept found in the knowledge base from the Centers for Disease Control.

The long description of the ICD-10-CM code in the ontology.

The level of confidence that Amazon Comprehend Medical has that the entity is accurately linked to an ICD-10-CM concept.

The detected attributes that relate to the entity. An extracted segment of the text that is an attribute of an entity, or otherwise related to an entity, such as the nature of a medical condition.

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 category of the entity. InferICD10CM detects entities in the MEDICAL_CONDITION category.

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.

The ICD-10-CM concepts that the entity could refer to, along with a score indicating the likelihood of the match.

The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.

The level of confidence that Amazon Comprehend Medical has in the accuracy of the detection.

The segment of input text that is matched to the detected entity.

Provides Contextual information for the entity. The traits recognized by InferICD10CM are DIAGNOSIS, SIGN, SYMPTOM, and NEGATION.

Describes the specific type of entity with category of entities. InferICD10CM detects entities of the type DX_NAME and TIME_EXPRESSION.

Provides a name or contextual description about the trait.

The level of confidence that Comprehend Medical; has that the segment of text is correctly recognized as a trait.

The path to the input data files in the S3 bucket.

The URI of the S3 bucket that contains the input data. The bucket must be in the same region as the API endpoint that you are calling.

Each file in the document collection must be less than 40 KB. You can store a maximum of 30 GB in the bucket.

The path to the output data files in the S3 bucket. Comprehend Medical; creates an output directory using the job ID so that the output from one job does not overwrite the output of another.

When you use the OutputDataConfig object with asynchronous operations, you specify the Amazon S3 location where you want to write the output data. The URI must be in the same region as the API endpoint that you are calling. The location is used as the prefix for the actual location of the output.

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 numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.

The level of confidence that Amazon Comprehend Medical has that the attribute is accurately linked to an entity.

The level of confidence that Comprehend Medical has that the segment of text is correctly recognized as an attribute.

The segment of input text which corresponds to the detected attribute.

Contextual information for the attribute. InferRxNorm recognizes the trait NEGATION for attributes, i.e. that the patient is not taking a specific dose or form of a medication.

The type of attribute. The types of attributes recognized by InferRxNorm are BRAND_NAME and GENERIC_NAME.

RxNorm concept ID, also known as the RxCUI.

The description of the RxNorm concept.

The level of confidence that Amazon Comprehend Medical has that the entity is accurately linked to the reported RxNorm concept.

The extracted attributes that relate to the entity. The attributes recognized by InferRxNorm are DOSAGE, DURATION, FORM, FREQUENCY, RATE, ROUTE_OR_MODE, and STRENGTH.

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 category of the entity. The recognized categories are GENERIC or BRAND_NAME.

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.

The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.

The RxNorm concepts that the entity could refer to, along with a score indicating the likelihood of the match.

The level of confidence that Amazon Comprehend Medical has in the accuracy of the detected entity.

The segment of input text extracted from which the entity was detected.

Contextual information for the entity.

Describes the specific type of entity. For InferRxNorm, the recognized entity type is MEDICATION.

Provides a name or contextual description about the trait.

The level of confidence that Amazon Comprehend Medical has in the accuracy of the detected trait.

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 category of the detected attribute. Possible categories include MEDICAL_CONDITION, ANATOMY, and TEST_TREATMENT_PROCEDURE.

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 numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.

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 SNOMED-CT concepts specific to an attribute, along with a score indicating the likelihood of the match.

The level of confidence that Comprehend Medical has that the segment of text is correctly recognized as an attribute.

The segment of input text extracted as this attribute.

Contextual information for an attribute. Examples include signs, symptoms, diagnosis, and negation.

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 numeric ID for the SNOMED-CT concept.

The description of the SNOMED-CT concept.

The level of confidence Comprehend Medical has that the entity should be linked to the identified SNOMED-CT concept.

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.

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 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 category of the detected entity. Possible categories are MEDICAL_CONDITION, ANATOMY, or TEST_TREATMENT_PROCEDURE.

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.

The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.

The SNOMED concepts that the entity could refer to, along with a score indicating the likelihood of the match.

The level of confidence that Comprehend Medical has in the accuracy of the detected entity.

The segment of input text extracted as this entity.

Contextual information for the entity.

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 name or contextual description of a detected trait.

The level of confidence that Comprehend Medical has in the accuracy of a detected trait.

Provides a name or contextual description about the trait.

The level of confidence that Comprehend Medical; has in the accuracy of this trait.

The specific attribute that has been extracted but not mapped to an entity.

The type of the unmapped attribute, could be one of the following values: "MEDICATION", "MEDICAL_CONDITION", "ANATOMY", "TEST_AND_TREATMENT_PROCEDURE" or "PROTECTED_HEALTH_INFORMATION".