使用 Amazon Comprehend Medical 的示例 AWS CLI - AWS SDK代码示例
操作

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使用 Amazon Comprehend Medical 的示例 AWS CLI

以下代码示例向您展示了如何使用 AWS Command Line Interface 与 Amazon Comprehend Medical 一起使用来执行操作和实现常见场景。

操作是大型程序的代码摘录，必须在上下文中运行。您可以通过操作了解如何调用单个服务函数，还可以通过函数相关场景的上下文查看操作。

每个示例都包含一个指向完整源代码的链接，您可以在其中找到有关如何在上下文中设置和运行代码的说明。

主题

操作

以下代码示例演示如何使用 describe-entities-detection-v2-job。

AWS CLI

描述实体检测作业

以下describe-entities-detection-v2-job示例显示了与异步实体检测作业关联的属性。

aws comprehendmedical describe-entities-detection-v2-job \
    --job-id "ab9887877365fe70299089371c043b96"

输出：

{
    "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"
    }
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 describe-icd10-cm-inference-job。

AWS CLI

描述一个 ICD -10-CM 的推理作业

以下describe-icd10-cm-inference-job示例描述了具有指定作业 ID 的请求推理作业的属性。

aws comprehendmedical describe-icd10-cm-inference-job \
    --job-id "5780034166536cdb52ffa3295a1b00a7"

输出：

{
    "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"
    }
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

  • 有关API详细信息，请参阅CmInferenceJob《AWS CLI 命令参考》中的 DescribeIcd10。

以下代码示例演示如何使用 describe-phi-detection-job。

AWS CLI

描述PHI检测作业

以下describe-phi-detection-job示例显示了与异步受保护的健康信息 (PHI) 检测作业关联的属性。

aws comprehendmedical describe-phi-detection-job \
    --job-id "4750034166536cdb52ffa3295a1b00a3"

输出：

{
    "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"
    }
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 describe-rx-norm-inference-job。

AWS CLI

描述 RxNorm 推理工作

以下describe-rx-norm-inference-job示例描述了具有指定作业 ID 的请求推理作业的属性。

aws comprehendmedical describe-rx-norm-inference-job \
    --job-id "eg8199877365fc70299089371c043b96"

输出：

{
    "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"
    }
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

以下代码示例演示如何使用 describe-snomedct-inference-job。

AWS CLI

描述 SNOMED CT 推理工作

以下describe-snomedct-inference-job示例描述了具有指定作业 ID 的请求推理作业的属性。

aws comprehendmedical describe-snomedct-inference-job \
    --job-id "2630034166536cdb52ffa3295a1b00a7"

输出：

{
    "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"
    }
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

以下代码示例演示如何使用 detect-entities-v2。

AWS CLI

示例 1：直接从文本中检测实体

以下detect-entities-v2示例显示了检测到的实体，并直接从输入文本中根据类型对其进行标记。

aws comprehendmedical detect-entities-v2 \
    --text "Sleeping trouble on present dosage of Clonidine. Severe rash on face and leg, slightly itchy."

输出：

{
    "Id": 0,
    "BeginOffset": 38,
    "EndOffset": 47,
    "Score": 0.9942955374717712,
    "Text": "Clonidine",
    "Category": "MEDICATION",
    "Type": "GENERIC_NAME",
    "Traits": []
}

有关更多信息，请参阅亚马逊 Compre hend Medical 开发者指南中的检测实体版本 2。

示例 2：检测文件路径中的实体

以下detect-entities-v2示例显示了检测到的实体，并根据文件路径中的类型对其进行标记。

aws comprehendmedical detect-entities-v2 \
    --text file://medical_entities.txt

medical_entities.txt 的内容：

{
    "Sleeping trouble on present dosage of Clonidine. Severe rash on face and leg, slightly itchy."
}

输出：

{
    "Id": 0,
    "BeginOffset": 38,
    "EndOffset": 47,
    "Score": 0.9942955374717712,
    "Text": "Clonidine",
    "Category": "MEDICATION",
    "Type": "GENERIC_NAME",
    "Traits": []
}

有关更多信息，请参阅亚马逊 Compre hend Medical 开发者指南中的检测实体版本 2。

  • 有关API详细信息，请参阅《AWS CLI 命令参考》中的 DetectEntitiesV2。

以下代码示例演示如何使用 detect-phi。

AWS CLI

示例 1：直接从文本中检测受保护的健康信息 (PHI)

以下detect-phi示例直接从输入文本中显示检测到的受保护健康信息 (PHI) 实体。

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."

输出：

{
    "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"
}

有关更多信息，请参阅《亚马逊 Compre hend Medical 开发者指南》PHI中的检测。

示例 2：直接从文件路径检测保护健康信息 (PHI)

以下detect-phi示例显示了从文件路径中检测到的受保护健康信息 (PHI) 实体。

aws comprehendmedical detect-phi \
    --text file://phi.txt

phi.txt 的内容：

"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."

输出：

{
    "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"
}

有关更多信息，请参阅《亚马逊 Compre hend Medical 开发者指南》PHI中的检测。

以下代码示例演示如何使用 infer-icd10-cm。

AWS CLI

示例 1：检测医疗状况实体并直接从文本链接到 ICD -10-CM 本体论

以下infer-icd10-cm示例标记了检测到的医疗状况实体，并将这些实体与2019年版《国际疾病分类临床修改》（ICD-10-CM）中的代码关联起来。

aws comprehendmedical infer-icd10-cm \
    --text "The patient complains of abdominal pain, has a long-standing history of diabetes treated with Micronase daily."

输出：

{
    "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"
}

有关更多信息，请参阅亚马逊 Comprehend Medical 开发者指南中的推断 ICD1 0-CM。

示例 2：检测医疗状况实体并从文件路径链接到 ICD -10-CM 本体论

以下infer-icd-10-cm示例标记了检测到的医疗状况实体，并将这些实体与2019年版《国际疾病分类临床修改》（ICD-10-CM）中的代码关联起来。

aws comprehendmedical infer-icd10-cm \
    --text file://icd10cm.txt

icd10cm.txt 的内容：

{
    "The patient complains of abdominal pain, has a long-standing history of diabetes treated with Micronase daily."
}

输出：

{
    "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"
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》中的 Infer-ICD1 0-CM。

  • 有关API详细信息，请参阅《AWS CLI 命令参考》中的 InferIcd10Cm。

以下代码示例演示如何使用 infer-rx-norm。

AWS CLI

示例 1：检测药物实体并 RxNorm 直接从文本链接到

以下infer-rx-norm示例显示并标记了检测到的药物实体，并将这些实体与美国国家医学图书馆 RxNorm 数据库中的概念标识符 (RxCUI) 关联起来。

aws comprehendmedical infer-rx-norm \
    --text "Patient reports taking Levothyroxine 125 micrograms p.o. once daily, but denies taking Synthroid."

输出：

{
    "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"
}

有关更多信息，请参阅亚马逊 Comprehend Medical 开发者指南 RxNorm中的推断。

示例 2：检测药物实体并 RxNorm 从文件路径链接到。

以下infer-rx-norm示例显示并标记了检测到的药物实体，并将这些实体与美国国家医学图书馆 RxNorm 数据库中的概念标识符 (RxCUI) 关联起来。

aws comprehendmedical infer-rx-norm \
    --text file://rxnorm.txt

rxnorm.txt 的内容：

{
    "Patient reports taking Levothyroxine 125 micrograms p.o. once daily, but denies taking Synthroid."
}

输出：

{
    "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"
}

有关更多信息，请参阅亚马逊 Comprehend Medical 开发者指南 RxNorm中的推断。

以下代码示例演示如何使用 infer-snomedct。

AWS CLI

示例：检测实体并直接从文本链接到 SNOMED CT 本体论

以下infer-snomedct示例说明如何检测医疗实体并将其与 2021-03 版本的系统化医学命名法，临床术语 (CT) 中的概念关联起来。SNOMED

aws comprehendmedical infer-snomedct \
    --text "The patient complains of abdominal pain, has a long-standing history of diabetes treated with Micronase daily."

输出：

{
    "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"
}

有关更多信息，请参阅亚马逊 Comprehend Medical 开发者指南SNOMEDCT中的推断。

以下代码示例演示如何使用 list-entities-detection-v2-jobs。

AWS CLI

列出实体检测任务

以下list-entities-detection-v2-jobs示例列出了当前的异步检测作业。

aws comprehendmedical list-entities-detection-v2-jobs

输出：

{
    "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"
        }
    ]
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 list-icd10-cm-inference-jobs。

AWS CLI

列出所有当前的 ICD -10-CM 推理作业

以下示例显示该list-icd10-cm-inference-jobs操作如何返回当前异步 ICD -10-CM 批量推理作业的列表。

aws comprehendmedical list-icd10-cm-inference-jobs

输出：

{
    "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"
        }
    ]
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

  • 有关API详细信息，请参阅CmInferenceJobs《AWS CLI 命令参考》中的 ListIcd10。

以下代码示例演示如何使用 list-phi-detection-jobs。

AWS CLI

列出受保护的健康信息 (PHI) 检测作业

以下list-phi-detection-jobs示例列出了当前受保护的健康信息 (PHI) 检测作业

aws comprehendmedical list-phi-detection-jobs

输出：

{
    "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"
        }
    ]
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 list-rx-norm-inference-jobs。

AWS CLI

列出所有当前的 Rx-Norm 推理作业

以下示例显示如何list-rx-norm-inference-jobs返回当前异步 Rx-Norm 批量推理作业的列表。

aws comprehendmedical list-rx-norm-inference-jobs

输出：

{
    "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"
        }
    ]
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

以下代码示例演示如何使用 list-snomedct-inference-jobs。

AWS CLI

列出所有 SNOMED CT 推理作业

以下示例显示该list-snomedct-inference-jobs操作如何返回当前异步 SNOMED CT 批量推理作业的列表。

aws comprehendmedical list-snomedct-inference-jobs

输出：

{
    "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"
        }
    ]
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

以下代码示例演示如何使用 start-entities-detection-v2-job。

AWS CLI

启动实体检测作业

以下start-entities-detection-v2-job示例启动异步实体检测作业。

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

输出：

{
    "JobId": "ab9887877365fe70299089371c043b96"
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 start-icd10-cm-inference-job。

AWS CLI

启动 ICD -10-CM 推理作业

以下start-icd10-cm-inference-job示例启动 ICD -10-CM 推理批量分析作业。

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

输出：

{
    "JobId": "ef7289877365fc70299089371c043b96"
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

  • 有关API详细信息，请参阅CmInferenceJob《AWS CLI 命令参考》中的 StartIcd10。

以下代码示例演示如何使用 start-phi-detection-job。

AWS CLI

启动PHI检测作业

以下start-phi-detection-job示例启动异步PHI实体检测作业。

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

输出：

{
    "JobId": "ab9887877365fe70299089371c043b96"
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 start-rx-norm-inference-job。

AWS CLI

开始 RxNorm 推理作业

以下start-rx-norm-inference-job示例启动 RxNorm 推理批量分析作业。

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

输出：

{
    "JobId": "eg8199877365fc70299089371c043b96"
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

以下代码示例演示如何使用 start-snomedct-inference-job。

AWS CLI

启动 C SNOMED T 推理作业

以下start-snomedct-inference-job示例启动 SNOMED CT 推理批量分析作业。

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

输出：

{
    "JobId": "dg7289877365fc70299089371c043b96"
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

以下代码示例演示如何使用 stop-entities-detection-v2-job。

AWS CLI

停止实体检测作业

以下stop-entities-detection-v2-job示例停止异步实体检测作业。

aws comprehendmedical stop-entities-detection-v2-job \
    --job-id "ab9887877365fe70299089371c043b96"

输出：

{
    "JobId": "ab9887877365fe70299089371c043b96"
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 stop-icd10-cm-inference-job。

AWS CLI

停止 ICD -10-CM 的推理作业

以下stop-icd10-cm-inference-job示例停止了 ICD -10-CM 的推理批量分析作业。

aws comprehendmedical stop-icd10-cm-inference-job \
    --job-id "4750034166536cdb52ffa3295a1b00a3"

输出：

{
    "JobId": "ef7289877365fc70299089371c043b96",
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

  • 有关API详细信息，请参阅CmInferenceJob《AWS CLI 命令参考》中的 StopIcd10。

以下代码示例演示如何使用 stop-phi-detection-job。

AWS CLI

停止受保护的健康信息 (PHI) 检测作业

以下stop-phi-detection-job示例停止异步受保护的健康信息 (PHI) 检测作业。

aws comprehendmedical stop-phi-detection-job \
    --job-id "4750034166536cdb52ffa3295a1b00a3"

输出：

{
    "JobId": "ab9887877365fe70299089371c043b96"
}

有关更多信息，请参阅《亚马逊 Comprehend Medical 开发者指南》APIs中的 B at c h。

以下代码示例演示如何使用 stop-rx-norm-inference-job。

AWS CLI

停止 RxNorm 推理作业

以下stop-rx-norm-inference-job示例停止了 ICD -10-CM 的推理批量分析作业。

aws comprehendmedical stop-rx-norm-inference-job \
    --job-id "eg8199877365fc70299089371c043b96"

输出：

{
    "JobId": "eg8199877365fc70299089371c043b96",
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。

以下代码示例演示如何使用 stop-snomedct-inference-job。

AWS CLI

停止 C SNOMED T 推理作业

以下stop-snomedct-inference-job示例停止 SNOMED CT 推理批量分析作业。

aws comprehendmedical stop-snomedct-inference-job \
    --job-id "8750034166436cdb52ffa3295a1b00a1"

输出：

{
    "JobId": "8750034166436cdb52ffa3295a1b00a1",
}

有关更多信息，请参阅 Amazon Comprehend Medical 开发者指南中的本体链接批量分析。