使用 Amazon Comprehend Medical 的示例 Amazon CLI - Amazon Command Line Interface
操作
Amazon Web Services 文档中描述的 Amazon Web Services 服务或功能可能因区域而异。要查看适用于中国区域的差异，请参阅 中国的 Amazon Web Services 服务入门 (PDF)。

本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

使用 Amazon Comprehend Medical 的示例 Amazon CLI

以下代码示例向您展示了如何使用 Amazon 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和跨服务示例的上下文查看操作。

场景是展示如何通过在同一服务中调用多个函数来完成特定任务任务的代码示例。

每个示例都包含一个指向的链接 GitHub，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。

Amazon 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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon CLI

描述 ICD-10-CM 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 的详细信息，请参阅CmInferenceJob《Amazon CLI 命令参考》中的 DescribeIcd10。

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

Amazon 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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon 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。

Amazon CLI

描述 SNOMED CT 推理作业

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。

Amazon 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 的详细信息，请参阅《Amazon CLI 命令参考》中的 DetectEntitiesV2。

以下代码示例演示如何使用 detect-phi。

Amazon 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 开发者指南中的检测 PHI。

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 开发者指南中的检测 PHI。

  • 有关 API 的详细信息，请参阅Amazon CLI 命令参考DetectPhi中的。

以下代码示例演示如何使用 infer-icd10-cm。

Amazon 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 Infer ICD10-CM。

示例 2：检测医疗状况实体并通过文件路径链接到 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"
}

欲了解更多信息，请参阅亚马逊 Comprehend Medical 开发者指南中的 infer-icd10-cm。

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

以下代码示例演示如何使用 infer-rx-norm。

Amazon 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，并将这些实体链接到美国国家医学图书馆 RxNorm 数据库中的概念标识符 (rxCUI)。

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，并将这些实体链接到美国国家医学图书馆 RxNorm 数据库中的概念标识符 (rxCUI)。

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。

  • 有关 API 的详细信息，请参阅Amazon CLI 命令参考InferRxNorm中的。

以下代码示例演示如何使用 infer-snomedct。

Amazon 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年版的系统化医学命名法，临床术语（SNOMED CT）中的概念关联起来。

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"
}

欲了解更多信息，请参阅亚马逊 C omprehend Medical 开发者指南中的 InfersnomedCT。

  • 有关 API 的详细信息，请参阅Amazon CLI 命令参考InferSnomedct中的。

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

Amazon 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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon CLI

列出所有当前的 ICD-10-CM 推理任务

以下示例显示了该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 的详细信息，请参阅CmInferenceJobs《Amazon CLI 命令参考》中的 ListIcd10。

以下代码示例演示如何使用 list-phi-detection-jobs。

Amazon CLI

列出受保护的健康信息 (PHI) 检测作业

以下list-phi-detection-jobs示例列出了当前的受保护健康信息 (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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon 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。

Amazon 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。

Amazon 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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon 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 的详细信息，请参阅CmInferenceJob《Amazon CLI 命令参考》中的 StartIcd10。

以下代码示例演示如何使用 start-phi-detection-job。

Amazon 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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon 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。

Amazon CLI

启动 SNOMED CT 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。

Amazon 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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon CLI

停止 ICD-10-CM 推理作业

以下stop-icd10-cm-inference-job示例停止 ICD-10-CM 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 的详细信息，请参阅CmInferenceJob《Amazon CLI 命令参考》中的 StopIcd10。

以下代码示例演示如何使用 stop-phi-detection-job。

Amazon 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 Compre hend Medical 开发者指南中的批处理 API。

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

Amazon CLI

停止 RxNorm 推理作业

以下stop-rx-norm-inference-job示例停止 ICD-10-CM 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。

Amazon CLI

停止 SNOMED CT 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。