# 使用 AWS CLI 的 Amazon Comprehend Medical 示例
以下代码示例演示如何通过将 AWS Command Line Interface与 Amazon Comprehend Medical 结合使用，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，您可以从中找到有关如何在上下文中设置和运行代码的说明。
**Topics**
+ [操作](#actions)
## 操作
### `describe-entities-detection-v2-job`
以下代码示例演示了如何使用 `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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [DescribeEntitiesDetectionV2Job](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/describe-entities-detection-v2-job.html)。
### `describe-icd10-cm-inference-job`
以下代码示例演示了如何使用 `describe-icd10-cm-inference-job`。
**AWS CLI**
**描述 ICD-10-CM 推理作业**
以下 `describe-icd10-cm-inference-job` 示例描述具有指定作业 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [DescribeIcd10CmInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/describe-icd10-cm-inference-job.html)。
### `describe-phi-detection-job`
以下代码示例演示了如何使用 `describe-phi-detection-job`。
**AWS CLI**
**描述 PHI 检测作业**
以下 `describe-phi-detection-job` 示例显示与异步受保护健康信息（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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [DescribePhiDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/describe-phi-detection-job.html)。
### `describe-rx-norm-inference-job`
以下代码示例演示了如何使用 `describe-rx-norm-inference-job`。
**AWS CLI**
**描述 RxNorm 推理作业**
以下 `describe-rx-norm-inference-job` 示例描述具有指定作业 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [DescribeRxNormInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/describe-rx-norm-inference-job.html)。
### `describe-snomedct-inference-job`
以下代码示例演示了如何使用 `describe-snomedct-inference-job`。
**AWS CLI**
**描述 SNOMED CT 推理作业**
以下 `describe-snomedct-inference-job` 示例描述具有指定作业 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [DescribeSnomedctInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/describe-snomedct-inference-job.html)。
### `detect-entities-v2`
以下代码示例演示了如何使用 `detect-entities-v2`。
**AWS CLI**
**示例 1：直接检测文本中的实体**
以下 `detect-entities-v2` 示例显示检测到的实体，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《Amazon Comprehend Medical 开发人员指南》**中的[检测实体版本 2](https://docs.aws.amazon.com/comprehend/latest/dg/extracted-med-info-V2.html)。
**示例 2：检测文件路径中的实体**
以下 `detect-entities-v2` 示例显示检测到的实体，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
```
的内容`medical_entities.txt`：
```
{
"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《Amazon Comprehend Medical 开发人员指南》**中的[检测实体版本 2](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-entitiesv2.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [DetectEntitiesV2](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/detect-entities-v2.html)。
### `detect-phi`
以下代码示例演示了如何使用 `detect-phi`。
**AWS CLI**
**示例 1：直接检测文本中的受保护健康信息（PHI）**
以下 `detect-phi` 示例直接显示输入文本中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《Amazon Comprehend Medical 开发人员指南》**中的[检测 PHI](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-phi.html)。
**示例 2：直接检测文件路径中的受保护健康信息（PHI）**
以下 `detect-phi` 示例显示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
```
的内容`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."
```
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《Amazon Comprehend Medical 开发人员指南》**中的[检测 PHI](https://docs.aws.amazon.com/comprehend/latest/dg/how-medical-phi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [DetectPhi](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/detect-phi.html)。
### `infer-icd10-cm`
以下代码示例演示了如何使用 `infer-icd10-cm`。
**AWS CLI**
**示例 1：直接检测文本中的医疗条件实体并将其链接到 ICD-10-CM 本体**
以下 `infer-icd10-cm` 示例标记检测到的医疗条件实体，并将这些实体与 2019 年版《国际疾病分类临床修订版（ICD-10-CM）》中的代码关联。
```
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《Amazon Comprehend Medical 开发人员指南》**中的 [Infer ICD10-CM](https://docs.aws.amazon.com/comprehend/latest/dg/ontology-linking-icd10.html)。
**示例 2：检测文件路径中的医疗条件实体并将其链接到 ICD-10-CM 本体**
以下 `infer-icd-10-cm` 示例标记检测到的医疗条件实体，并将这些实体与 2019 年版《国际疾病分类临床修订版（ICD-10-CM）》中的代码关联。
```
aws comprehendmedical infer-icd10-cm \
--text file://icd10cm.txt
```
的内容`icd10cm.txt`：
```
{
"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《Amazon Comprehend Medical 开发人员指南》**中的 [Infer-ICD10-CM](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontology-icd10.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [InferIcd10Cm](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/infer-icd10-cm.html)。
### `infer-rx-norm`
以下代码示例演示了如何使用 `infer-rx-norm`。
**AWS CLI**
**示例 1：直接检测文本中的药物实体并将其链接到 RxNorm**
以下 `infer-rx-norm` 示例显示和标记检测到的药物实体，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。
```
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《Amazon Comprehend Medical 开发人员指南》**中的[推理 RxNorm](https://docs.aws.amazon.com/comprehend/latest/dg/ontology-linking-rxnorm.html)。
**示例 2：检测文件路径中的药物实体并将其链接到 RxNorm**
以下 `infer-rx-norm` 示例显示和标记检测到的药物实体，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。
```
aws comprehendmedical infer-rx-norm \
--text file://rxnorm.txt
```
的内容`rxnorm.txt`：
```
{
"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《Amazon Comprehend Medical 开发人员指南》**中的[推理 RxNorm](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontology-RxNorm.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [InferRxNorm](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/infer-rx-norm.html)。
### `infer-snomedct`
以下代码示例演示了如何使用 `infer-snomedct`。
**AWS CLI**
**示例：直接检测文本中的实体并将其链接到 SNOMED CT 本体**
以下 `infer-snomedct` 示例演示如何检测医学实体，并将其与 2021-03 版《医学系统命名法，临床术语（SNOMED CT）》中的概念关联。
```
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《Amazon Comprehend Medical 开发人员指南》**中的 [InferSNOMEDCT](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontology-linking-snomed.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [InferSnomedct](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/infer-snomedct.html)。
### `list-entities-detection-v2-jobs`
以下代码示例演示了如何使用 `list-entities-detection-v2-jobs`。
**AWS CLI**
**列出实体检测作业**
以下 `list-entities-detection-v2-jobs` 示例列出当前异步检测作业。
```
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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [ListEntitiesDetectionV2Jobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/list-entities-detection-v2-jobs.html)。
### `list-icd10-cm-inference-jobs`
以下代码示例演示了如何使用 `list-icd10-cm-inference-jobs`。
**AWS CLI**
**列出所有当前 ICD-10-CM 推理作业**
以下示例演示 `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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [ListIcd10CmInferenceJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/list-icd10-cm-inference-jobs.html)。
### `list-phi-detection-jobs`
以下代码示例演示了如何使用 `list-phi-detection-jobs`。
**AWS CLI**
**列出受保护健康信息（PHI）检测作业**
以下 `list-phi-detection-jobs` 示例列出当前受保护健康信息（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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [ListPhiDetectionJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/list-phi-detection-jobs.html)。
### `list-rx-norm-inference-jobs`
以下代码示例演示了如何使用 `list-rx-norm-inference-jobs`。
**AWS CLI**
**列出所有当前的 Rx-Norm 推理作业**
以下示例演示 `list-rx-norm-inference-jobs` 如何返回当前异步 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [ListRxNormInferenceJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/list-rx-norm-inference-jobs.html)。
### `list-snomedct-inference-jobs`
以下代码示例演示了如何使用 `list-snomedct-inference-jobs`。
**AWS CLI**
**列出所有 SNOMED CT 推理作业**
以下示例演示 `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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [ListSnomedctInferenceJobs](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/list-snomedct-inference-jobs.html)。
### `start-entities-detection-v2-job`
以下代码示例演示了如何使用 `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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StartEntitiesDetectionV2Job](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/start-entities-detection-v2-job.html)。
### `start-icd10-cm-inference-job`
以下代码示例演示了如何使用 `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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StartIcd10CmInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/start-icd10-cm-inference-job.html)。
### `start-phi-detection-job`
以下代码示例演示了如何使用 `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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StartPhiDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/start-phi-detection-job.html)。
### `start-rx-norm-inference-job`
以下代码示例演示了如何使用 `start-rx-norm-inference-job`。
**AWS CLI**
**启动 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StartRxNormInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/start-rx-norm-inference-job.html)。
### `start-snomedct-inference-job`
以下代码示例演示了如何使用 `start-snomedct-inference-job`。
**AWS 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StartSnomedctInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/start-snomedct-inference-job.html)。
### `stop-entities-detection-v2-job`
以下代码示例演示了如何使用 `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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StopEntitiesDetectionV2Job](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/stop-entities-detection-v2-job.html)。
### `stop-icd10-cm-inference-job`
以下代码示例演示了如何使用 `stop-icd10-cm-inference-job`。
**AWS 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StopIcd10CmInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/stop-icd10-cm-inference-job.html)。
### `stop-phi-detection-job`
以下代码示例演示了如何使用 `stop-phi-detection-job`。
**AWS CLI**
**停止受保护健康信息（PHI）检测作业**
以下 `stop-phi-detection-job` 示例停止异步受保护健康信息（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《Amazon Comprehend Medical 开发人员指南》**中的[批处理 API](https://docs.aws.amazon.com/comprehend-medical/latest/dev/textanalysis-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StopPhiDetectionJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/stop-phi-detection-job.html)。
### `stop-rx-norm-inference-job`
以下代码示例演示了如何使用 `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 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StopRxNormInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/stop-rx-norm-inference-job.html)。
### `stop-snomedct-inference-job`
以下代码示例演示了如何使用 `stop-snomedct-inference-job`。
**AWS 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 开发人员指南》**中的[本体链接批量分析](https://docs.aws.amazon.com/comprehend-medical/latest/dev/ontologies-batchapi.html)。
+ 有关 API 详细信息，请参阅《AWS CLI 命令参考》**中的 [StopSnomedctInferenceJob](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/comprehendmedical/stop-snomedct-inference-job.html)。