Amazon Comprehend Medical examples using AWS CLI
The following code examples show you how to perform actions and implement common scenarios by using the AWS Command Line Interface with Amazon Comprehend Medical.
Actions are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in their related scenarios.
Each example includes a link to the complete source code, where you can find instructions on how to set up and run the code in context.
Topics
Actions
The following code example shows how to use describe-entities-detection-v2-job
.
- AWS CLI
-
To describe an entities detection job
The following
describe-entities-detection-v2-job
example displays the properties associated with an asynchronous entity detection job.aws comprehendmedical describe-entities-detection-v2-job \ --job-id
"ab9887877365fe70299089371c043b96"
Output:
{ "ComprehendMedicalAsyncJobProperties": { "JobId": "ab9887877365fe70299089371c043b96", "JobStatus": "COMPLETED", "SubmitTime": "2020-03-18T21:20:15.614000+00:00", "EndTime": "2020-03-18T21:27:07.350000+00:00", "ExpirationTime": "2020-07-16T21:20:15+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "867139942017-EntitiesDetection-ab9887877365fe70299089371c043b96/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "DetectEntitiesModelV20190930" } }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see DescribeEntitiesDetectionV2Job
in AWS CLI Command Reference.
-
The following code example shows how to use describe-icd10-cm-inference-job
.
- AWS CLI
-
To describe an ICD-10-CM inference job
The following
describe-icd10-cm-inference-job
example describes the properties of the requested inference job with the specified job-id.aws comprehendmedical describe-icd10-cm-inference-job \ --job-id
"5780034166536cdb52ffa3295a1b00a7"
Output:
{ "ComprehendMedicalAsyncJobProperties": { "JobId": "5780034166536cdb52ffa3295a1b00a7", "JobStatus": "COMPLETED", "SubmitTime": "2020-05-18T21:20:15.614000+00:00", "EndTime": "2020-05-18T21:27:07.350000+00:00", "ExpirationTime": "2020-09-16T21:20:15+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "0.1.0" } }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see DescribeIcd10CmInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use describe-phi-detection-job
.
- AWS CLI
-
To describe a PHI detection job
The following
describe-phi-detection-job
example displays the properties associated with an asynchronous protected health information (PHI) detection job.aws comprehendmedical describe-phi-detection-job \ --job-id
"4750034166536cdb52ffa3295a1b00a3"
Output:
{ "ComprehendMedicalAsyncJobProperties": { "JobId": "4750034166536cdb52ffa3295a1b00a3", "JobStatus": "COMPLETED", "SubmitTime": "2020-03-19T20:38:37.594000+00:00", "EndTime": "2020-03-19T20:45:07.894000+00:00", "ExpirationTime": "2020-07-17T20:38:37+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "867139942017-PHIDetection-4750034166536cdb52ffa3295a1b00a3/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "PHIModelV20190903" } }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see DescribePhiDetectionJob
in AWS CLI Command Reference.
-
The following code example shows how to use describe-rx-norm-inference-job
.
- AWS CLI
-
To describe an RxNorm inference job
The following
describe-rx-norm-inference-job
example describes the properties of the requested inference job with the specified job-id.aws comprehendmedical describe-rx-norm-inference-job \ --job-id
"eg8199877365fc70299089371c043b96"
Output:
{ "ComprehendMedicalAsyncJobProperties": { "JobId": "g8199877365fc70299089371c043b96", "JobStatus": "COMPLETED", "SubmitTime": "2020-05-18T21:20:15.614000+00:00", "EndTime": "2020-05-18T21:27:07.350000+00:00", "ExpirationTime": "2020-09-16T21:20:15+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "0.0.0" } }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see DescribeRxNormInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use describe-snomedct-inference-job
.
- AWS CLI
-
To describe an SNOMED CT inference job
The following
describe-snomedct-inference-job
example describes the properties of the requested inference job with the specified job-id.aws comprehendmedical describe-snomedct-inference-job \ --job-id
"2630034166536cdb52ffa3295a1b00a7"
Output:
{ "ComprehendMedicalAsyncJobProperties": { "JobId": "2630034166536cdb52ffa3295a1b00a7", "JobStatus": "COMPLETED", "SubmitTime": "2021-12-18T21:20:15.614000+00:00", "EndTime": "2021-12-18T21:27:07.350000+00:00", "ExpirationTime": "2022-05-16T21:20:15+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "0.1.0" } }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see DescribeSnomedctInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use detect-entities-v2
.
- AWS CLI
-
Example 1: To detect entities directly from text
The following
detect-entities-v2
example shows the detected entities and labels them according to type, directly from input text.aws comprehendmedical detect-entities-v2 \ --text
"Sleeping trouble on present dosage of Clonidine. Severe rash on face and leg, slightly itchy."
Output:
{ "Id": 0, "BeginOffset": 38, "EndOffset": 47, "Score": 0.9942955374717712, "Text": "Clonidine", "Category": "MEDICATION", "Type": "GENERIC_NAME", "Traits": [] }
For more information, see Detect Entities Version 2 in the Amazon Comprehend Medical Developer Guide.
Example 2: To detect entities from a file path
The following
detect-entities-v2
example shows the detected entities and labels them according to type from a file path.aws comprehendmedical detect-entities-v2 \ --text
file://medical_entities.txt
Contents of
medical_entities.txt
:{ "Sleeping trouble on present dosage of Clonidine. Severe rash on face and leg, slightly itchy." }
Output:
{ "Id": 0, "BeginOffset": 38, "EndOffset": 47, "Score": 0.9942955374717712, "Text": "Clonidine", "Category": "MEDICATION", "Type": "GENERIC_NAME", "Traits": [] }
For more information, see Detect Entities Version 2 in the Amazon Comprehend Medical Developer Guide.
-
For API details, see DetectEntitiesV2
in AWS CLI Command Reference.
-
The following code example shows how to use detect-phi
.
- AWS CLI
-
Example 1: To detect protected health information (PHI) directly from text
The following
detect-phi
example displays the detected protected health information (PHI) entities directly from input text.aws comprehendmedical detect-phi \ --text
"Patient Carlos Salazar presented with rash on his upper extremities and dry cough. He lives at 100 Main Street, Anytown, USA where he works from his home as a carpenter."
Output:
{ "Entities": [ { "Id": 0, "BeginOffset": 8, "EndOffset": 21, "Score": 0.9914507269859314, "Text": "Carlos Salazar", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "NAME", "Traits": [] }, { "Id": 1, "BeginOffset": 94, "EndOffset": 109, "Score": 0.871849775314331, "Text": "100 Main Street, Anytown, USA", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "ADDRESS", "Traits": [] }, { "Id": 2, "BeginOffset": 145, "EndOffset": 154, "Score": 0.8302185535430908, "Text": "carpenter", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "PROFESSION", "Traits": [] } ], "ModelVersion": "0.0.0" }
For more information, see Detect PHI in the Amazon Comprehend Medical Developer Guide.
Example 2: To detect protect health information (PHI) directly from a file path
The following
detect-phi
example shows the detected protected health information (PHI) entities from a file path.aws comprehendmedical detect-phi \ --text
file://phi.txt
Contents of
phi.txt
:"Patient Carlos Salazar presented with a rash on his upper extremities and a dry cough. He lives at 100 Main Street, Anytown, USA, where he works from his home as a carpenter."
Output:
{ "Entities": [ { "Id": 0, "BeginOffset": 8, "EndOffset": 21, "Score": 0.9914507269859314, "Text": "Carlos Salazar", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "NAME", "Traits": [] }, { "Id": 1, "BeginOffset": 94, "EndOffset": 109, "Score": 0.871849775314331, "Text": "100 Main Street, Anytown, USA", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "ADDRESS", "Traits": [] }, { "Id": 2, "BeginOffset": 145, "EndOffset": 154, "Score": 0.8302185535430908, "Text": "carpenter", "Category": "PROTECTED_HEALTH_INFORMATION", "Type": "PROFESSION", "Traits": [] } ], "ModelVersion": "0.0.0" }
For more information, see Detect PHI in the Amazon Comprehend Medical Developer Guide.
-
For API details, see DetectPhi
in AWS CLI Command Reference.
-
The following code example shows how to use infer-icd10-cm
.
- AWS CLI
-
Example 1: To detect medical condition entities and link to the ICD-10-CM Ontology directly from text
The following
infer-icd10-cm
example labels the detected medical condition entities and links those entities with codes in the 2019 edition of the International Classification of Diseases Clinical Modification (ICD-10-CM).aws comprehendmedical infer-icd10-cm \ --text
"The patient complains of abdominal pain, has a long-standing history of diabetes treated with Micronase daily."
Output:
{ "Entities": [ { "Id": 0, "Text": "abdominal pain", "Category": "MEDICAL_CONDITION", "Type": "DX_NAME", "Score": 0.9475538730621338, "BeginOffset": 28, "EndOffset": 42, "Attributes": [], "Traits": [ { "Name": "SYMPTOM", "Score": 0.6724207401275635 } ], "ICD10CMConcepts": [ { "Description": "Unspecified abdominal pain", "Code": "R10.9", "Score": 0.6904221177101135 }, { "Description": "Epigastric pain", "Code": "R10.13", "Score": 0.1364113688468933 }, { "Description": "Generalized abdominal pain", "Code": "R10.84", "Score": 0.12508003413677216 }, { "Description": "Left lower quadrant pain", "Code": "R10.32", "Score": 0.10063883662223816 }, { "Description": "Lower abdominal pain, unspecified", "Code": "R10.30", "Score": 0.09933677315711975 } ] }, { "Id": 1, "Text": "diabetes", "Category": "MEDICAL_CONDITION", "Type": "DX_NAME", "Score": 0.9899052977561951, "BeginOffset": 75, "EndOffset": 83, "Attributes": [], "Traits": [ { "Name": "DIAGNOSIS", "Score": 0.9258432388305664 } ], "ICD10CMConcepts": [ { "Description": "Type 2 diabetes mellitus without complications", "Code": "E11.9", "Score": 0.7158446311950684 }, { "Description": "Family history of diabetes mellitus", "Code": "Z83.3", "Score": 0.5704703330993652 }, { "Description": "Family history of other endocrine, nutritional and metabolic diseases", "Code": "Z83.49", "Score": 0.19856023788452148 }, { "Description": "Type 1 diabetes mellitus with ketoacidosis without coma", "Code": "E10.10", "Score": 0.13285516202449799 }, { "Description": "Type 2 diabetes mellitus with hyperglycemia", "Code": "E11.65", "Score": 0.0993388369679451 } ] } ], "ModelVersion": "0.1.0" }
For more information, see Infer ICD10-CM in the Amazon Comprehend Medical Developer Guide.
Example 2: To detect medical condition entities and link to the ICD-10-CM Ontology from a file pathway
The following
infer-icd-10-cm
example labels the detected medical condition entities and links those entities with codes in the 2019 edition of the International Classification of Diseases Clinical Modification (ICD-10-CM).aws comprehendmedical infer-icd10-cm \ --text
file://icd10cm.txt
Contents of
icd10cm.txt
:{ "The patient complains of abdominal pain, has a long-standing history of diabetes treated with Micronase daily." }
Output:
{ "Entities": [ { "Id": 0, "Text": "abdominal pain", "Category": "MEDICAL_CONDITION", "Type": "DX_NAME", "Score": 0.9475538730621338, "BeginOffset": 28, "EndOffset": 42, "Attributes": [], "Traits": [ { "Name": "SYMPTOM", "Score": 0.6724207401275635 } ], "ICD10CMConcepts": [ { "Description": "Unspecified abdominal pain", "Code": "R10.9", "Score": 0.6904221177101135 }, { "Description": "Epigastric pain", "Code": "R10.13", "Score": 0.1364113688468933 }, { "Description": "Generalized abdominal pain", "Code": "R10.84", "Score": 0.12508003413677216 }, { "Description": "Left lower quadrant pain", "Code": "R10.32", "Score": 0.10063883662223816 }, { "Description": "Lower abdominal pain, unspecified", "Code": "R10.30", "Score": 0.09933677315711975 } ] }, { "Id": 1, "Text": "diabetes", "Category": "MEDICAL_CONDITION", "Type": "DX_NAME", "Score": 0.9899052977561951, "BeginOffset": 75, "EndOffset": 83, "Attributes": [], "Traits": [ { "Name": "DIAGNOSIS", "Score": 0.9258432388305664 } ], "ICD10CMConcepts": [ { "Description": "Type 2 diabetes mellitus without complications", "Code": "E11.9", "Score": 0.7158446311950684 }, { "Description": "Family history of diabetes mellitus", "Code": "Z83.3", "Score": 0.5704703330993652 }, { "Description": "Family history of other endocrine, nutritional and metabolic diseases", "Code": "Z83.49", "Score": 0.19856023788452148 }, { "Description": "Type 1 diabetes mellitus with ketoacidosis without coma", "Code": "E10.10", "Score": 0.13285516202449799 }, { "Description": "Type 2 diabetes mellitus with hyperglycemia", "Code": "E11.65", "Score": 0.0993388369679451 } ] } ], "ModelVersion": "0.1.0" }
For more information, see Infer-ICD10-CM in the Amazon Comprehend Medical Developer Guide.
-
For API details, see InferIcd10Cm
in AWS CLI Command Reference.
-
The following code example shows how to use infer-rx-norm
.
- AWS CLI
-
Example 1: To detect medication entities and link to RxNorm directly from text
The following
infer-rx-norm
example shows and labels the detected medication entities and links those entities to concept identifiers (RxCUI) from the National Library of Medicine RxNorm database.aws comprehendmedical infer-rx-norm \ --text
"Patient reports taking Levothyroxine 125 micrograms p.o. once daily, but denies taking Synthroid."
Output:
{ "Entities": [ { "Id": 0, "Text": "Levothyroxine", "Category": "MEDICATION", "Type": "GENERIC_NAME", "Score": 0.9996285438537598, "BeginOffset": 23, "EndOffset": 36, "Attributes": [ { "Type": "DOSAGE", "Score": 0.9892290830612183, "RelationshipScore": 0.9997978806495667, "Id": 1, "BeginOffset": 37, "EndOffset": 51, "Text": "125 micrograms", "Traits": [] }, { "Type": "ROUTE_OR_MODE", "Score": 0.9988924860954285, "RelationshipScore": 0.998291552066803, "Id": 2, "BeginOffset": 52, "EndOffset": 56, "Text": "p.o.", "Traits": [] }, { "Type": "FREQUENCY", "Score": 0.9953463673591614, "RelationshipScore": 0.9999889135360718, "Id": 3, "BeginOffset": 57, "EndOffset": 67, "Text": "once daily", "Traits": [] } ], "Traits": [], "RxNormConcepts": [ { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet", "Code": "966224", "Score": 0.9912070631980896 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Capsule", "Code": "966405", "Score": 0.8698278665542603 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet [Synthroid]", "Code": "966191", "Score": 0.7448257803916931 }, { "Description": "levothyroxine", "Code": "10582", "Score": 0.7050482630729675 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet [Levoxyl]", "Code": "966190", "Score": 0.6921631693840027 } ] }, { "Id": 4, "Text": "Synthroid", "Category": "MEDICATION", "Type": "BRAND_NAME", "Score": 0.9946461319923401, "BeginOffset": 86, "EndOffset": 95, "Attributes": [], "Traits": [ { "Name": "NEGATION", "Score": 0.5167351961135864 } ], "RxNormConcepts": [ { "Description": "Synthroid", "Code": "224920", "Score": 0.9462039470672607 }, { "Description": "Levothyroxine Sodium 0.088 MG Oral Tablet [Synthroid]", "Code": "966282", "Score": 0.8309829235076904 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet [Synthroid]", "Code": "966191", "Score": 0.4945160448551178 }, { "Description": "Levothyroxine Sodium 0.05 MG Oral Tablet [Synthroid]", "Code": "966247", "Score": 0.3674522042274475 }, { "Description": "Levothyroxine Sodium 0.025 MG Oral Tablet [Synthroid]", "Code": "966158", "Score": 0.2588822841644287 } ] } ], "ModelVersion": "0.0.0" }
For more information, see Infer RxNorm in the Amazon Comprehend Medical Developer Guide.
Example 2: To detect medication entities and link to RxNorm from a file path.
The following
infer-rx-norm
example shows and labels the detected medication entities and links those entities to concept identifiers (RxCUI) from the National Library of Medicine RxNorm database.aws comprehendmedical infer-rx-norm \ --text
file://rxnorm.txt
Contents of
rxnorm.txt
:{ "Patient reports taking Levothyroxine 125 micrograms p.o. once daily, but denies taking Synthroid." }
Output:
{ "Entities": [ { "Id": 0, "Text": "Levothyroxine", "Category": "MEDICATION", "Type": "GENERIC_NAME", "Score": 0.9996285438537598, "BeginOffset": 23, "EndOffset": 36, "Attributes": [ { "Type": "DOSAGE", "Score": 0.9892290830612183, "RelationshipScore": 0.9997978806495667, "Id": 1, "BeginOffset": 37, "EndOffset": 51, "Text": "125 micrograms", "Traits": [] }, { "Type": "ROUTE_OR_MODE", "Score": 0.9988924860954285, "RelationshipScore": 0.998291552066803, "Id": 2, "BeginOffset": 52, "EndOffset": 56, "Text": "p.o.", "Traits": [] }, { "Type": "FREQUENCY", "Score": 0.9953463673591614, "RelationshipScore": 0.9999889135360718, "Id": 3, "BeginOffset": 57, "EndOffset": 67, "Text": "once daily", "Traits": [] } ], "Traits": [], "RxNormConcepts": [ { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet", "Code": "966224", "Score": 0.9912070631980896 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Capsule", "Code": "966405", "Score": 0.8698278665542603 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet [Synthroid]", "Code": "966191", "Score": 0.7448257803916931 }, { "Description": "levothyroxine", "Code": "10582", "Score": 0.7050482630729675 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet [Levoxyl]", "Code": "966190", "Score": 0.6921631693840027 } ] }, { "Id": 4, "Text": "Synthroid", "Category": "MEDICATION", "Type": "BRAND_NAME", "Score": 0.9946461319923401, "BeginOffset": 86, "EndOffset": 95, "Attributes": [], "Traits": [ { "Name": "NEGATION", "Score": 0.5167351961135864 } ], "RxNormConcepts": [ { "Description": "Synthroid", "Code": "224920", "Score": 0.9462039470672607 }, { "Description": "Levothyroxine Sodium 0.088 MG Oral Tablet [Synthroid]", "Code": "966282", "Score": 0.8309829235076904 }, { "Description": "Levothyroxine Sodium 0.125 MG Oral Tablet [Synthroid]", "Code": "966191", "Score": 0.4945160448551178 }, { "Description": "Levothyroxine Sodium 0.05 MG Oral Tablet [Synthroid]", "Code": "966247", "Score": 0.3674522042274475 }, { "Description": "Levothyroxine Sodium 0.025 MG Oral Tablet [Synthroid]", "Code": "966158", "Score": 0.2588822841644287 } ] } ], "ModelVersion": "0.0.0" }
For more information, see Infer RxNorm in the Amazon Comprehend Medical Developer Guide.
-
For API details, see InferRxNorm
in AWS CLI Command Reference.
-
The following code example shows how to use infer-snomedct
.
- AWS CLI
-
Example: To detect entities and link to the SNOMED CT Ontology directly from text
The following
infer-snomedct
example shows how to detect medical entities and link them to concepts from the 2021-03 version of the Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT).aws comprehendmedical infer-snomedct \ --text
"The patient complains of abdominal pain, has a long-standing history of diabetes treated with Micronase daily."
Output:
{ "Entities": [ { "Id": 3, "BeginOffset": 26, "EndOffset": 40, "Score": 0.9598260521888733, "Text": "abdominal pain", "Category": "MEDICAL_CONDITION", "Type": "DX_NAME", "Traits": [ { "Name": "SYMPTOM", "Score": 0.6819021701812744 } ] }, { "Id": 4, "BeginOffset": 73, "EndOffset": 81, "Score": 0.9905840158462524, "Text": "diabetes", "Category": "MEDICAL_CONDITION", "Type": "DX_NAME", "Traits": [ { "Name": "DIAGNOSIS", "Score": 0.9255214333534241 } ] }, { "Id": 1, "BeginOffset": 95, "EndOffset": 104, "Score": 0.6371926665306091, "Text": "Micronase", "Category": "MEDICATION", "Type": "BRAND_NAME", "Traits": [], "Attributes": [ { "Type": "FREQUENCY", "Score": 0.9761165380477905, "RelationshipScore": 0.9984188079833984, "RelationshipType": "FREQUENCY", "Id": 2, "BeginOffset": 105, "EndOffset": 110, "Text": "daily", "Category": "MEDICATION", "Traits": [] } ] } ], "UnmappedAttributes": [], "ModelVersion": "1.0.0" }
For more information, see InferSNOMEDCT in the Amazon Comprehend Medical Developer Guide.
-
For API details, see InferSnomedct
in AWS CLI Command Reference.
-
The following code example shows how to use list-entities-detection-v2-jobs
.
- AWS CLI
-
To list entities detection jobs
The following
list-entities-detection-v2-jobs
example lists current asynchronous detection jobs.aws comprehendmedical list-entities-detection-v2-jobs
Output:
{ "ComprehendMedicalAsyncJobPropertiesList": [ { "JobId": "ab9887877365fe70299089371c043b96", "JobStatus": "COMPLETED", "SubmitTime": "2020-03-19T20:38:37.594000+00:00", "EndTime": "2020-03-19T20:45:07.894000+00:00", "ExpirationTime": "2020-07-17T20:38:37+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "867139942017-EntitiesDetection-ab9887877365fe70299089371c043b96/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "DetectEntitiesModelV20190930" } ] }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see ListEntitiesDetectionV2Jobs
in AWS CLI Command Reference.
-
The following code example shows how to use list-icd10-cm-inference-jobs
.
- AWS CLI
-
To list all current ICD-10-CM inference jobs
The following example shows how the
list-icd10-cm-inference-jobs
operation returns a list of current asynchronous ICD-10-CM batch inference jobs.aws comprehendmedical list-icd10-cm-inference-jobs
Output:
{ "ComprehendMedicalAsyncJobPropertiesList": [ { "JobId": "5780034166536cdb52ffa3295a1b00a7", "JobStatus": "COMPLETED", "SubmitTime": "2020-05-19T20:38:37.594000+00:00", "EndTime": "2020-05-19T20:45:07.894000+00:00", "ExpirationTime": "2020-09-17T20:38:37+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "0.1.0" } ] }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see ListIcd10CmInferenceJobs
in AWS CLI Command Reference.
-
The following code example shows how to use list-phi-detection-jobs
.
- AWS CLI
-
To list protected health information (PHI) detection jobs
The following
list-phi-detection-jobs
example lists current protected health information (PHI) detection jobsaws comprehendmedical list-phi-detection-jobs
Output:
{ "ComprehendMedicalAsyncJobPropertiesList": [ { "JobId": "4750034166536cdb52ffa3295a1b00a3", "JobStatus": "COMPLETED", "SubmitTime": "2020-03-19T20:38:37.594000+00:00", "EndTime": "2020-03-19T20:45:07.894000+00:00", "ExpirationTime": "2020-07-17T20:38:37+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "867139942017-PHIDetection-4750034166536cdb52ffa3295a1b00a3/" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "PHIModelV20190903" } ] }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see ListPhiDetectionJobs
in AWS CLI Command Reference.
-
The following code example shows how to use list-rx-norm-inference-jobs
.
- AWS CLI
-
To list all current Rx-Norm inference jobs
The following example shows how
list-rx-norm-inference-jobs
returns a list of current asynchronous Rx-Norm batch inference jobs.aws comprehendmedical list-rx-norm-inference-jobs
Output:
{ "ComprehendMedicalAsyncJobPropertiesList": [ { "JobId": "4980034166536cfb52gga3295a1b00a3", "JobStatus": "COMPLETED", "SubmitTime": "2020-05-19T20:38:37.594000+00:00", "EndTime": "2020-05-19T20:45:07.894000+00:00", "ExpirationTime": "2020-09-17T20:38:37+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "0.0.0" } ] }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see ListRxNormInferenceJobs
in AWS CLI Command Reference.
-
The following code example shows how to use list-snomedct-inference-jobs
.
- AWS CLI
-
To list all SNOMED CT inference jobs
The following example shows how the
list-snomedct-inference-jobs
operation returns a list of current asynchronous SNOMED CT batch inference jobs.aws comprehendmedical list-snomedct-inference-jobs
Output:
{ "ComprehendMedicalAsyncJobPropertiesList": [ { "JobId": "5780034166536cdb52ffa3295a1b00a7", "JobStatus": "COMPLETED", "SubmitTime": "2020-05-19T20:38:37.594000+00:00", "EndTime": "2020-05-19T20:45:07.894000+00:00", "ExpirationTime": "2020-09-17T20:38:37+00:00", "InputDataConfig": { "S3Bucket": "comp-med-input", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "OutputDataConfig": { "S3Bucket": "comp-med-output", "S3Key": "AKIAIOSFODNN7EXAMPLE" }, "LanguageCode": "en", "DataAccessRoleArn": "arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole", "ModelVersion": "0.1.0" } ] }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see ListSnomedctInferenceJobs
in AWS CLI Command Reference.
-
The following code example shows how to use start-entities-detection-v2-job
.
- AWS CLI
-
To start an entities detection job
The following
start-entities-detection-v2-job
example starts an asynchronous entity detection job.aws comprehendmedical start-entities-detection-v2-job \ --input-data-config
"S3Bucket=comp-med-input"
\ --output-data-config"S3Bucket=comp-med-output"
\ --data-access-role-arnarn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole
\ --language-codeen
Output:
{ "JobId": "ab9887877365fe70299089371c043b96" }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StartEntitiesDetectionV2Job
in AWS CLI Command Reference.
-
The following code example shows how to use start-icd10-cm-inference-job
.
- AWS CLI
-
To start an ICD-10-CM inference job
The following
start-icd10-cm-inference-job
example starts an ICD-10-CM inference batch analysis job.aws comprehendmedical start-icd10-cm-inference-job \ --input-data-config
"S3Bucket=comp-med-input"
\ --output-data-config"S3Bucket=comp-med-output"
\ --data-access-role-arnarn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole
\ --language-codeen
Output:
{ "JobId": "ef7289877365fc70299089371c043b96" }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StartIcd10CmInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use start-phi-detection-job
.
- AWS CLI
-
To start a PHI detection job
The following
start-phi-detection-job
example starts an asynchronous PHI entity detection job.aws comprehendmedical start-phi-detection-job \ --input-data-config
"S3Bucket=comp-med-input"
\ --output-data-config"S3Bucket=comp-med-output"
\ --data-access-role-arnarn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole
\ --language-codeen
Output:
{ "JobId": "ab9887877365fe70299089371c043b96" }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StartPhiDetectionJob
in AWS CLI Command Reference.
-
The following code example shows how to use start-rx-norm-inference-job
.
- AWS CLI
-
To start an RxNorm inference job
The following
start-rx-norm-inference-job
example starts an RxNorm inference batch analysis job.aws comprehendmedical start-rx-norm-inference-job \ --input-data-config
"S3Bucket=comp-med-input"
\ --output-data-config"S3Bucket=comp-med-output"
\ --data-access-role-arnarn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole
\ --language-codeen
Output:
{ "JobId": "eg8199877365fc70299089371c043b96" }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StartRxNormInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use start-snomedct-inference-job
.
- AWS CLI
-
To start an SNOMED CT inference job
The following
start-snomedct-inference-job
example starts a SNOMED CT inference batch analysis job.aws comprehendmedical start-snomedct-inference-job \ --input-data-config
"S3Bucket=comp-med-input"
\ --output-data-config"S3Bucket=comp-med-output"
\ --data-access-role-arnarn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole
\ --language-codeen
Output:
{ "JobId": "dg7289877365fc70299089371c043b96" }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StartSnomedctInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use stop-entities-detection-v2-job
.
- AWS CLI
-
To stop an entity detection job
The following
stop-entities-detection-v2-job
example stops an asynchronous entity detection job.aws comprehendmedical stop-entities-detection-v2-job \ --job-id
"ab9887877365fe70299089371c043b96"
Output:
{ "JobId": "ab9887877365fe70299089371c043b96" }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StopEntitiesDetectionV2Job
in AWS CLI Command Reference.
-
The following code example shows how to use stop-icd10-cm-inference-job
.
- AWS CLI
-
To stop an ICD-10-CM inference job
The following
stop-icd10-cm-inference-job
example stops an ICD-10-CM inference batch analysis job.aws comprehendmedical stop-icd10-cm-inference-job \ --job-id
"4750034166536cdb52ffa3295a1b00a3"
Output:
{ "JobId": "ef7289877365fc70299089371c043b96", }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StopIcd10CmInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use stop-phi-detection-job
.
- AWS CLI
-
To stop a protected health information (PHI) detection job
The following
stop-phi-detection-job
example stops an asynchronous protected health information (PHI) detection job.aws comprehendmedical stop-phi-detection-job \ --job-id
"4750034166536cdb52ffa3295a1b00a3"
Output:
{ "JobId": "ab9887877365fe70299089371c043b96" }
For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StopPhiDetectionJob
in AWS CLI Command Reference.
-
The following code example shows how to use stop-rx-norm-inference-job
.
- AWS CLI
-
To stop an RxNorm inference job
The following
stop-rx-norm-inference-job
example stops an ICD-10-CM inference batch analysis job.aws comprehendmedical stop-rx-norm-inference-job \ --job-id
"eg8199877365fc70299089371c043b96"
Output:
{ "JobId": "eg8199877365fc70299089371c043b96", }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StopRxNormInferenceJob
in AWS CLI Command Reference.
-
The following code example shows how to use stop-snomedct-inference-job
.
- AWS CLI
-
To stop a SNOMED CT inference job
The following
stop-snomedct-inference-job
example stops a SNOMED CT inference batch analysis job.aws comprehendmedical stop-snomedct-inference-job \ --job-id
"8750034166436cdb52ffa3295a1b00a1"
Output:
{ "JobId": "8750034166436cdb52ffa3295a1b00a1", }
For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.
-
For API details, see StopSnomedctInferenceJob
in AWS CLI Command Reference.
-