Smaws_Client_ComprehendMedical.TypesThe filter that you specified for the operation is invalid. Check the filter values that you entered and try your request again.
type nonrec entity_sub_type = | REC_DRUG_USE| ALCOHOL_CONSUMPTION| TOBACCO_USE| ALLERGIES| RACE_ETHNICITY| GENDER| AMOUNT| TIME_TO_TREATMENT_NAME| TIME_TO_PROCEDURE_NAME| TIME_TO_TEST_NAME| TIME_TO_DX_NAME| TIME_TO_MEDICATION_NAME| TIME_EXPRESSION| QUANTITY| QUALITY| DIRECTION| SYSTEM_ORGAN_SITE| PROFESSION| ADDRESS| URL| ID| IDENTIFIER| EMAIL| PHONE_OR_FAX| CONTACT_POINT| AGE| DATE| TREATMENT_NAME| PROCEDURE_NAME| TEST_UNIT| TEST_UNITS| TEST_VALUE| TEST_NAME| ACUITY| RATE| STRENGTH| BRAND_NAME| GENERIC_NAME| DURATION| FREQUENCY| FORM| ROUTE_OR_MODE| DOSAGE| DX_NAME| NAMEtype nonrec trait = {score : float_ option;The level of confidence that Amazon Comprehend Medical has in the accuracy of this trait.
*)name : attribute_name option;Provides a name or contextual description about the trait.
*)}Provides contextual information about the extracted entity.
type nonrec trait_list = trait listtype nonrec attribute = {traits : trait_list option;Contextual information for this attribute.
*)category : entity_type option;The category of attribute.
*)text : string_ option;The segment of input text extracted as this attribute.
*)end_offset : integer 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.
*)begin_offset : integer 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.
*)id : integer option;The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)relationship_type : relationship_type option;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.
relationship_score : float_ option;The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
*)type_ : entity_sub_type option;The type of attribute.
*)}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 nonrec unmapped_attribute = {attribute : attribute option;The specific attribute that has been extracted but not mapped to an entity.
*)type_ : entity_type option;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".
*)}An attribute that was extracted, but Amazon Comprehend Medical was unable to relate to an entity.
type nonrec unmapped_attribute_list = unmapped_attribute listYou have made too many requests within a short period of time. Wait for a short time and then try your request again. Contact customer support for more information about a service limit increase.
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type nonrec stop_snomedct_inference_job_response = {job_id : job_id option;The identifier generated for the job. To get the status of job, use this identifier with the DescribeSNOMEDCTInferenceJob operation.
*)}type nonrec stop_snomedct_inference_job_request = {job_id : job_id;The job id of the asynchronous InferSNOMEDCT job to be stopped.
*)}The resource identified by the specified Amazon Resource Name (ARN) was not found. Check the ARN and try your request again.
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type nonrec stop_rx_norm_inference_job_response = {job_id : job_id option;The identifier generated for the job. To get the status of job, use this identifier with the DescribeRxNormInferenceJob operation.
}type nonrec stop_phi_detection_job_response = {job_id : job_id option;The identifier of the PHI detection job that was stopped.
*)}type nonrec stop_phi_detection_job_request = {job_id : job_id;The identifier of the PHI detection job to stop.
*)}type nonrec stop_icd10cm_inference_job_response = {job_id : job_id option;The identifier generated for the job. To get the status of job, use this identifier with the DescribeICD10CMInferenceJob operation.
}type nonrec stop_entities_detection_v2_job_response = {job_id : job_id option;The identifier of the medical entities detection job that was stopped.
*)}type nonrec stop_entities_detection_v2_job_request = {job_id : job_id;The identifier of the medical entities job to stop.
*)}type nonrec start_snomedct_inference_job_response = {job_id : job_id option;The identifier generated for the job. To get the status of a job, use this identifier with the StartSNOMEDCTInferenceJob operation.
*)}type nonrec input_data_config = {s3_key : s3_key option;The path to the input data files in the S3 bucket.
*)s3_bucket : 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.
*)}The input properties for an entities detection job. This includes the name of the S3 bucket and the path to the files to be analyzed.
type nonrec output_data_config = {s3_key : s3_key option;The path to the output data files in the S3 bucket. Amazon Comprehend Medical creates an output directory using the job ID so that the output from one job does not overwrite the output of another.
*)s3_bucket : s3_bucket;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 output properties for a detection job.
type nonrec start_snomedct_inference_job_request = {language_code : language_code;The language of the input documents. All documents must be in the same language.
*)kms_key : kms_key option;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.
*)client_request_token : client_request_token_string option;A unique identifier for the request. If you don't set the client request token, Amazon Comprehend Medical generates one.
*)job_name : job_name option;The user generated name the asynchronous InferSNOMEDCT job.
*)data_access_role_arn : iam_role_arn;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.
*)output_data_config : output_data_config;input_data_config : input_data_config;}type nonrec start_rx_norm_inference_job_response = {job_id : job_id option;The identifier of the job.
*)}type nonrec start_rx_norm_inference_job_request = {language_code : language_code;The language of the input documents. All documents must be in the same language.
*)kms_key : kms_key option;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.
*)client_request_token : client_request_token_string option;A unique identifier for the request. If you don't set the client request token, Amazon Comprehend Medical generates one.
*)job_name : job_name option;The identifier of the job.
*)data_access_role_arn : iam_role_arn;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. For more information, see Role-Based Permissions Required for Asynchronous Operations.
*)output_data_config : output_data_config;Specifies where to send the output files.
*)input_data_config : input_data_config;Specifies the format and location of the input data for the job.
*)}type nonrec start_phi_detection_job_response = {job_id : job_id option;The identifier generated for the job. To get the status of a job, use this identifier with the DescribePHIDetectionJob operation.
}type nonrec start_phi_detection_job_request = {language_code : language_code;The language of the input documents. All documents must be in the same language.
*)kms_key : kms_key option;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.
*)client_request_token : client_request_token_string option;A unique identifier for the request. If you don't set the client request token, Amazon Comprehend Medical generates one.
*)job_name : job_name option;The identifier of the job.
*)data_access_role_arn : iam_role_arn;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. For more information, see Role-Based Permissions Required for Asynchronous Operations.
*)output_data_config : output_data_config;Specifies where to send the output files.
*)input_data_config : input_data_config;Specifies the format and location of the input data for the job.
*)}type nonrec start_icd10cm_inference_job_response = {job_id : job_id option;The identifier generated for the job. To get the status of a job, use this identifier with the StartICD10CMInferenceJob operation.
}type nonrec start_icd10cm_inference_job_request = {language_code : language_code;The language of the input documents. All documents must be in the same language.
*)kms_key : kms_key option;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.
*)client_request_token : client_request_token_string option;A unique identifier for the request. If you don't set the client request token, Amazon Comprehend Medical generates one.
*)job_name : job_name option;The identifier of the job.
*)data_access_role_arn : iam_role_arn;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. For more information, see Role-Based Permissions Required for Asynchronous Operations.
*)output_data_config : output_data_config;Specifies where to send the output files.
*)input_data_config : input_data_config;Specifies the format and location of the input data for the job.
*)}type nonrec start_entities_detection_v2_job_response = {job_id : job_id option;The identifier generated for the job. To get the status of a job, use this identifier with the DescribeEntitiesDetectionV2Job operation.
}type nonrec start_entities_detection_v2_job_request = {language_code : language_code;The language of the input documents. All documents must be in the same language. Amazon Comprehend Medical processes files in US English (en).
*)kms_key : kms_key option;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.
*)client_request_token : client_request_token_string option;A unique identifier for the request. If you don't set the client request token, Amazon Comprehend Medical generates one for you.
*)job_name : job_name option;The identifier of the job.
*)data_access_role_arn : iam_role_arn;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. For more information, see Role-Based Permissions Required for Asynchronous Operations.
*)output_data_config : output_data_config;The output configuration that specifies where to send the output files.
*)input_data_config : input_data_config;The input configuration that specifies the format and location of the input data for the job.
*)}The Amazon Comprehend Medical service is temporarily unavailable. Please wait and then retry your request.
type nonrec snomedct_trait = {score : float_ option;The level of confidence that Amazon Comprehend Medical has in the accuracy of a detected trait.
*)name : snomedct_trait_name option;The name or contextual description of a detected trait.
*)}Contextual information for an entity.
type nonrec snomedct_trait_list = snomedct_trait listtype nonrec snomedct_concept = {score : float_ option;The level of confidence Amazon Comprehend Medical has that the entity should be linked to the identified SNOMED-CT concept.
*)code : string_ option;The numeric ID for the SNOMED-CT concept.
*)description : string_ option;The description of the SNOMED-CT concept.
*)}The SNOMED-CT concepts that the entity could refer to, along with a score indicating the likelihood of the match.
type nonrec snomedct_concept_list = snomedct_concept listtype nonrec snomedct_attribute = {snomedct_concepts : snomedct_concept_list option;The SNOMED-CT concepts specific to an attribute, along with a score indicating the likelihood of the match.
*)traits : snomedct_trait_list option;Contextual information for an attribute. Examples include signs, symptoms, diagnosis, and negation.
*)text : string_ option;The segment of input text extracted as this attribute.
*)end_offset : integer 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.
*)begin_offset : integer 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.
*)id : integer option;The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)relationship_type : snomedct_relationship_type option;The type of relationship that exists between the entity and the related attribute.
*)relationship_score : float_ option;The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
*)type_ : snomedct_attribute_type 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.
*)category : snomedct_entity_category option;The category of the detected attribute. Possible categories include MEDICAL_CONDITION, ANATOMY, and TEST_TREATMENT_PROCEDURE.
*)}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.
type nonrec snomedct_attribute_list = snomedct_attribute listtype nonrec snomedct_entity = {snomedct_concepts : snomedct_concept_list option;The SNOMED concepts that the entity could refer to, along with a score indicating the likelihood of the match.
*)traits : snomedct_trait_list option;Contextual information for the entity.
*)attributes : snomedct_attribute_list option;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.
*)end_offset : integer option;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.
*)begin_offset : integer option;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.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has in the accuracy of the detected entity.
*)type_ : snomedct_entity_type option;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.
*)category : snomedct_entity_category option;The category of the detected entity. Possible categories are MEDICAL_CONDITION, ANATOMY, or TEST_TREATMENT_PROCEDURE.
*)text : ontology_linking_bounded_length_string option;The segment of input text extracted as this entity.
*)id : integer option;The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)}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.
type nonrec snomedct_entity_list = snomedct_entity listtype nonrec snomedct_details = {version_date : string_ option;The version date of the SNOMED-CT ontology used.
*)language : string_ option;The language used in the SNOMED-CT ontology. All Amazon Comprehend Medical operations are US English (en).
*)edition : string_ option;The edition of SNOMED-CT used. The edition used for the InferSNOMEDCT editions is the US edition.
*)}The information about the revision of the SNOMED-CT ontology in the response. Specifically, the details include the SNOMED-CT edition, language, and version date.
type nonrec rx_norm_trait = {score : float_ option;The level of confidence that Amazon Comprehend Medical has in the accuracy of the detected trait.
*)name : rx_norm_trait_name option;Provides a name or contextual description about the trait.
*)}The contextual information for the entity. InferRxNorm recognizes the trait NEGATION, which is any indication that the patient is not taking a medication.
type nonrec rx_norm_trait_list = rx_norm_trait listtype nonrec rx_norm_attribute = {traits : rx_norm_trait_list option;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.
text : string_ option;The segment of input text which corresponds to the detected attribute.
*)end_offset : integer 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.
*)begin_offset : integer 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.
*)id : integer option;The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)relationship_score : float_ option;The level of confidence that Amazon Comprehend Medical has that the attribute is accurately linked to an entity.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
*)type_ : rx_norm_attribute_type option;The type of attribute. The types of attributes recognized by InferRxNorm are BRAND_NAME and GENERIC_NAME.
}The extracted attributes that relate to this entity. The attributes recognized by InferRxNorm are DOSAGE, DURATION, FORM, FREQUENCY, RATE, ROUTE_OR_MODE.
type nonrec rx_norm_attribute_list = rx_norm_attribute listtype nonrec rx_norm_concept = {score : float_ option;The level of confidence that Amazon Comprehend Medical has that the entity is accurately linked to the reported RxNorm concept.
*)code : string_ option;RxNorm concept ID, also known as the RxCUI.
*)description : string_ option;The description of the RxNorm concept.
*)}The RxNorm concept that the entity could refer to, along with a score indicating the likelihood of the match.
type nonrec rx_norm_concept_list = rx_norm_concept listtype nonrec rx_norm_entity = {rx_norm_concepts : rx_norm_concept_list option;The RxNorm concepts that the entity could refer to, along with a score indicating the likelihood of the match.
*)traits : rx_norm_trait_list option;Contextual information for the entity.
*)attributes : rx_norm_attribute_list option;The extracted attributes that relate to the entity. The attributes recognized by InferRxNorm are DOSAGE, DURATION, FORM, FREQUENCY, RATE, ROUTE_OR_MODE, and STRENGTH.
end_offset : integer option;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.
*)begin_offset : integer option;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.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has in the accuracy of the detected entity.
*)type_ : rx_norm_entity_type option;Describes the specific type of entity. For InferRxNorm, the recognized entity type is MEDICATION.
category : rx_norm_entity_category option;The category of the entity. The recognized categories are GENERIC or BRAND_NAME.
text : ontology_linking_bounded_length_string option;The segment of input text extracted from which the entity was detected.
*)id : integer option;The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)}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.
type nonrec rx_norm_entity_list = rx_norm_entity listtype nonrec comprehend_medical_async_job_properties = {model_version : model_version option;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.
*)kms_key : kms_key option;The AWS Key Management Service key, if any, used to encrypt the output files.
*)manifest_file_path : manifest_file_path option;The path to the file that describes the results of a batch job.
*)data_access_role_arn : iam_role_arn option;The Amazon Resource Name (ARN) that gives Amazon Comprehend Medical read access to your input data.
*)language_code : language_code option;The language code of the input documents.
*)output_data_config : output_data_config option;The output data configuration that you supplied when you created the detection job.
*)input_data_config : input_data_config option;The input data configuration that you supplied when you created the detection job.
*)expiration_time : timestamp option;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.
end_time : timestamp option;The time that the detection job completed.
*)submit_time : timestamp option;The time that the detection job was submitted for processing.
*)message : any_length_string option;A description of the status of a job.
*)job_status : job_status option;The current status of the detection job. If the status is FAILED, the Message field shows the reason for the failure.
job_name : job_name option;The name that you assigned to the detection job.
*)job_id : job_id option;The identifier assigned to the detection job.
*)}Provides information about a detection job.
type nonrec comprehend_medical_async_job_properties_list =
comprehend_medical_async_job_properties listtype nonrec list_snomedct_inference_jobs_response = {next_token : string_ option;Identifies the next page of results to return.
*)comprehend_medical_async_job_properties_list : comprehend_medical_async_job_properties_list
option;A list containing the properties of each job that is returned.
*)}type nonrec comprehend_medical_async_job_filter = {submit_time_after : timestamp option;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.
*)submit_time_before : timestamp option;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.
*)job_status : job_status option;Filters the list of jobs based on job status. Returns only jobs with the specified status.
*)job_name : job_name option;Filters on the name of the job.
*)}Provides information for filtering a list of detection jobs.
type nonrec list_snomedct_inference_jobs_request = {max_results : max_results_integer option;The maximum number of results to return in each page. The default is 100.
*)next_token : string_ option;Identifies the next page of InferSNOMEDCT results to return.
*)filter : comprehend_medical_async_job_filter option;}type nonrec list_rx_norm_inference_jobs_response = {next_token : string_ option;Identifies the next page of results to return.
*)comprehend_medical_async_job_properties_list : comprehend_medical_async_job_properties_list
option;The maximum number of results to return in each page. The default is 100.
*)}type nonrec list_rx_norm_inference_jobs_request = {max_results : max_results_integer option;Identifies the next page of results to return.
*)next_token : string_ option;Identifies the next page of results to return.
*)filter : comprehend_medical_async_job_filter option;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.
*)}type nonrec list_phi_detection_jobs_response = {next_token : string_ option;Identifies the next page of results to return.
*)comprehend_medical_async_job_properties_list : comprehend_medical_async_job_properties_list
option;A list containing the properties of each job returned.
*)}type nonrec list_phi_detection_jobs_request = {max_results : max_results_integer option;The maximum number of results to return in each page. The default is 100.
*)next_token : string_ option;Identifies the next page of results to return.
*)filter : comprehend_medical_async_job_filter option;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.
*)}type nonrec list_icd10cm_inference_jobs_response = {next_token : string_ option;Identifies the next page of results to return.
*)comprehend_medical_async_job_properties_list : comprehend_medical_async_job_properties_list
option;A list containing the properties of each job that is returned.
*)}type nonrec list_icd10cm_inference_jobs_request = {max_results : max_results_integer option;The maximum number of results to return in each page. The default is 100.
*)next_token : string_ option;Identifies the next page of results to return.
*)filter : comprehend_medical_async_job_filter option;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.
*)}type nonrec list_entities_detection_v2_jobs_response = {next_token : string_ option;Identifies the next page of results to return.
*)comprehend_medical_async_job_properties_list : comprehend_medical_async_job_properties_list
option;A list containing the properties of each job returned.
*)}type nonrec list_entities_detection_v2_jobs_request = {max_results : max_results_integer option;The maximum number of results to return in each page. The default is 100.
*)next_token : string_ option;Identifies the next page of results to return.
*)filter : comprehend_medical_async_job_filter option;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 input text was not in valid UTF-8 character encoding. Check your text then retry your request.
type nonrec characters = {original_text_characters : integer option;The number of characters present in the input text document as processed by Amazon Comprehend Medical.
*)}The number of characters in the input text to be analyzed.
type nonrec infer_snomedct_response = {characters : characters option;The number of characters in the input request documentation.
*)snomedct_details : snomedct_details option;The details of the SNOMED-CT revision, including the edition, language, and version date.
*)model_version : string_ option;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.
*)pagination_token : string_ option;If the result of the request is truncated, the pagination token can be used to fetch the next page of entities.
*)entities : snomedct_entity_list;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 Amazon Comprehend Medical has in the detection and analysis. Attributes and traits of the entity are also returned.
*)}type nonrec infer_snomedct_request = {text : ontology_linking_bounded_length_string;The input text to be analyzed using InferSNOMEDCT.
*)}type nonrec infer_rx_norm_response = {model_version : string_ option;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.
*)pagination_token : string_ option;If the result of the previous request to InferRxNorm was truncated, include the PaginationToken to fetch the next page of medication entities.
entities : rx_norm_entity_list;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.
*)}type nonrec infer_rx_norm_request = {text : ontology_linking_bounded_length_string;The input text used for analysis.
*)}type nonrec icd10cm_trait = {score : float_ option;The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as a trait.
*)name : icd10cm_trait_name option;Provides a name or contextual description about the trait.
*)}Contextual information for the entity. The traits recognized by InferICD10CM are DIAGNOSIS, SIGN, SYMPTOM, and NEGATION.
type nonrec icd10cm_trait_list = icd10cm_trait listtype nonrec icd10cm_attribute = {relationship_type : icd10cm_relationship_type option;The type of relationship between the entity and attribute. Type for the relationship can be either of OVERLAP or SYSTEM_ORGAN_SITE.
category : icd10cm_entity_type option;The category of attribute. Can be either of DX_NAME or TIME_EXPRESSION.
traits : icd10cm_trait_list option;The contextual information for the attribute. The traits recognized by InferICD10CM are DIAGNOSIS, SIGN, SYMPTOM, and NEGATION.
text : string_ option;The segment of input text which contains the detected attribute.
*)end_offset : integer 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.
*)begin_offset : integer 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.
*)id : integer option;The numeric identifier for this attribute. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)relationship_score : float_ option;The level of confidence that Amazon Comprehend Medical has that this attribute is correctly related to this entity.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has that the segment of text is correctly recognized as an attribute.
*)type_ : icd10cm_attribute_type option;The type of attribute. InferICD10CM detects entities of the type DX_NAME.
}The detected attributes that relate to an entity. This includes an extracted segment of the text that is an attribute of an entity, or otherwise related to an entity. InferICD10CM detects the following attributes: Direction, System, Organ or Site, and Acuity.
type nonrec icd10cm_attribute_list = icd10cm_attribute listtype nonrec icd10cm_concept = {score : float_ option;The level of confidence that Amazon Comprehend Medical has that the entity is accurately linked to an ICD-10-CM concept.
*)code : string_ option;The ICD-10-CM code that identifies the concept found in the knowledge base from the Centers for Disease Control.
*)description : string_ option;The long description of the ICD-10-CM code in the ontology.
*)}The ICD-10-CM concepts that the entity could refer to, along with a score indicating the likelihood of the match.
type nonrec icd10cm_concept_list = icd10cm_concept listtype nonrec icd10cm_entity = {icd10cm_concepts : icd10cm_concept_list option;The ICD-10-CM concepts that the entity could refer to, along with a score indicating the likelihood of the match.
*)traits : icd10cm_trait_list option;Provides Contextual information for the entity. The traits recognized by InferICD10CM are DIAGNOSIS, SIGN, SYMPTOM, and NEGATION.
attributes : icd10cm_attribute_list option;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.
*)end_offset : integer option;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.
*)begin_offset : integer option;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.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has in the accuracy of the detection.
*)type_ : icd10cm_entity_type option;Describes the specific type of entity with category of entities. InferICD10CM detects entities of the type DX_NAME and TIME_EXPRESSION.
category : icd10cm_entity_category option;The category of the entity. InferICD10CM detects entities in the MEDICAL_CONDITION category.
text : ontology_linking_bounded_length_string option;The segment of input text that is matched to the detected entity.
*)id : integer option;The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)}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.
type nonrec icd10cm_entity_list = icd10cm_entity listtype nonrec infer_icd10cm_response = {model_version : string_ option;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.
*)pagination_token : string_ option;If the result of the previous request to InferICD10CM was truncated, include the PaginationToken to fetch the next page of medical condition entities.
entities : icd10cm_entity_list;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.
*)}type nonrec infer_icd10cm_request = {text : ontology_linking_bounded_length_string;The input text used for analysis.
*)}type nonrec attribute_list = attribute listtype nonrec entity = {attributes : attribute_list option;The extracted attributes that relate to this entity.
*)traits : trait_list option;Contextual information for the entity.
*)type_ : entity_sub_type option;Describes the specific type of entity with category of entities.
*)category : entity_type option;The category of the entity.
*)text : string_ option;The segment of input text extracted as this entity.
*)score : float_ option;The level of confidence that Amazon Comprehend Medical has in the accuracy of the detection.
*)end_offset : integer option;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.
*)begin_offset : integer option;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.
*)id : integer option;The numeric identifier for the entity. This is a monotonically increasing id unique within this response rather than a global unique identifier.
*)}Provides information about an extracted medical entity.
type nonrec entity_list = entity listtype nonrec detect_phi_response = {model_version : 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.
*)pagination_token : string_ option;If the result of the previous request to DetectPHI was truncated, include the PaginationToken to fetch the next page of PHI entities.
entities : entity_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.
*)}type nonrec detect_phi_request = {text : bounded_length_string;A UTF-8 text string containing the clinical content being examined for PHI entities.
*)}type nonrec detect_entities_v2_response = {model_version : 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.
*)pagination_token : string_ option;If the result to the DetectEntitiesV2 operation was truncated, include the PaginationToken to fetch the next page of entities.
unmapped_attributes : unmapped_attribute_list option;Attributes extracted from the input text that couldn't be related to an entity.
*)entities : entity_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.
*)}type nonrec detect_entities_v2_request = {text : bounded_length_string;A UTF-8 string containing the clinical content being examined for entities.
*)}type nonrec detect_entities_response = {model_version : 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.
*)pagination_token : string_ option;If the result of the previous request to DetectEntities was truncated, include the PaginationToken to fetch the next page of entities.
unmapped_attributes : unmapped_attribute_list option;Attributes extracted from the input text that we were unable to relate to an entity.
*)entities : entity_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.
*)}type nonrec detect_entities_request = {text : bounded_length_string;A UTF-8 text string containing the clinical content being examined for entities.
*)}type nonrec describe_snomedct_inference_job_response = {comprehend_medical_async_job_properties : comprehend_medical_async_job_properties
option;}type nonrec describe_snomedct_inference_job_request = {job_id : job_id;The identifier that Amazon Comprehend Medical generated for the job. The StartSNOMEDCTInferenceJob operation returns this identifier in its response.
*)}type nonrec describe_rx_norm_inference_job_response = {comprehend_medical_async_job_properties : comprehend_medical_async_job_properties
option;An object that contains the properties associated with a detection job.
*)}type nonrec describe_rx_norm_inference_job_request = {job_id : job_id;The identifier that Amazon Comprehend Medical generated for the job. The StartRxNormInferenceJob operation returns this identifier in its response.
*)}type nonrec describe_phi_detection_job_response = {comprehend_medical_async_job_properties : comprehend_medical_async_job_properties
option;An object that contains the properties associated with a detection job.
*)}type nonrec describe_phi_detection_job_request = {job_id : job_id;The identifier that Amazon Comprehend Medical generated for the job. The StartPHIDetectionJob operation returns this identifier in its response.
}type nonrec describe_icd10cm_inference_job_response = {comprehend_medical_async_job_properties : comprehend_medical_async_job_properties
option;An object that contains the properties associated with a detection job.
*)}type nonrec describe_icd10cm_inference_job_request = {job_id : job_id;The identifier that Amazon Comprehend Medical generated for the job. The StartICD10CMInferenceJob operation returns this identifier in its response.
}type nonrec describe_entities_detection_v2_job_response = {comprehend_medical_async_job_properties : comprehend_medical_async_job_properties
option;An object that contains the properties associated with a detection job.
*)}type nonrec describe_entities_detection_v2_job_request = {job_id : job_id;The identifier that Amazon Comprehend Medical generated for the job. The StartEntitiesDetectionV2Job operation returns this identifier in its response.
}