Amazon Comprehend Medical examples using Amazon CLI - Amazon Command Line Interface
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Amazon Comprehend Medical examples using Amazon CLI

The following code examples show you how to perform actions and implement common scenarios by using the Amazon 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 and cross-service examples.

Scenarios are code examples that show you how to accomplish a specific task by calling multiple functions within the same service.

Each example includes a link to GitHub, 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.

Amazon 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.

The following code example shows how to use describe-icd10-cm-inference-job.

Amazon 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.

The following code example shows how to use describe-phi-detection-job.

Amazon 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.

The following code example shows how to use describe-rx-norm-inference-job.

Amazon 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.

The following code example shows how to use describe-snomedct-inference-job.

Amazon 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.

The following code example shows how to use detect-entities-v2.

Amazon 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.

The following code example shows how to use detect-phi.

Amazon 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 Amazon CLI Command Reference.

The following code example shows how to use infer-icd10-cm.

Amazon 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 Amazon CLI Command Reference.

The following code example shows how to use infer-rx-norm.

Amazon 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 Amazon CLI Command Reference.

The following code example shows how to use infer-snomedct.

Amazon 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 Amazon CLI Command Reference.

The following code example shows how to use list-entities-detection-v2-jobs.

Amazon 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.

The following code example shows how to use list-icd10-cm-inference-jobs.

Amazon 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.

The following code example shows how to use list-phi-detection-jobs.

Amazon CLI

To list protected health information (PHI) detection jobs

The following list-phi-detection-jobs example lists current protected health information (PHI) detection jobs

aws 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.

The following code example shows how to use list-rx-norm-inference-jobs.

Amazon 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.

The following code example shows how to use list-snomedct-inference-jobs.

Amazon 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.

The following code example shows how to use start-entities-detection-v2-job.

Amazon 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-arn arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole \
    --language-code en

Output:

{
    "JobId": "ab9887877365fe70299089371c043b96"
}

For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.

The following code example shows how to use start-icd10-cm-inference-job.

Amazon 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-arn arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole \
    --language-code en

Output:

{
    "JobId": "ef7289877365fc70299089371c043b96"
}

For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.

The following code example shows how to use start-phi-detection-job.

Amazon 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-arn arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole \
    --language-code en

Output:

{
    "JobId": "ab9887877365fe70299089371c043b96"
}

For more information, see Batch APIs in the Amazon Comprehend Medical Developer Guide.

The following code example shows how to use start-rx-norm-inference-job.

Amazon 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-arn arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole \
    --language-code en

Output:

{
    "JobId": "eg8199877365fc70299089371c043b96"
}

For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.

The following code example shows how to use start-snomedct-inference-job.

Amazon 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-arn arn:aws:iam::867139942017:role/ComprehendMedicalBatchProcessingRole \
    --language-code en

Output:

{
    "JobId": "dg7289877365fc70299089371c043b96"
}

For more information, see Ontology linking batch analysis in the Amazon Comprehend Medical Developer Guide.

The following code example shows how to use stop-entities-detection-v2-job.

Amazon 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.

The following code example shows how to use stop-icd10-cm-inference-job.

Amazon 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.

The following code example shows how to use stop-phi-detection-job.

Amazon 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.

The following code example shows how to use stop-rx-norm-inference-job.

Amazon 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.

The following code example shows how to use stop-snomedct-inference-job.

Amazon 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.