Quickstart: Using Text Analytics for health client library and REST API
This article contains Text Analytics for health quickstarts that help with using the supported client libraries, C#, Java, NodeJS, and Python as well as with using the REST API.
Tip
You can use Language Studio to try Language service features without needing to write code.
Reference documentation | Additional samples | Package (NuGet) | Library source code
Use this quickstart to create a Text Analytics for health application with the client library for .NET. In the following example, you will create a C# application that can identify medical entities, relations, and assertions that appear in text.
Prerequisites
- Azure subscription - Create one for free
- The Visual Studio IDE
- Once you have your Azure subscription, create a Language resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource.
- You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
- You can use the free pricing tier (
Free F0) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to theStandard Spricing tier for production. You can also start with theStandard Spricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit Language Service Pricing.
Setting up
Create a new .NET Core application
Using the Visual Studio IDE, create a new .NET Core console app. This will create a "Hello World" project with a single C# source file: program.cs.
Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. In the package manager that opens select Browse and search for Azure.AI.TextAnalytics. Select version 5.2.0, and then Install. You can also use the Package Manager Console.
Code example
Copy the following code into your program.cs file. Remember to replace the key variable with the key for your resource, and replace the endpoint variable with the endpoint for your resource. Then run the code.
Important
Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.
Important
Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Cognitive Services security article for more information.
using Azure;
using System;
using Azure.AI.TextAnalytics;
using System.Collections.Generic;
using System.Threading.Tasks;
namespace Example
{
class Program
{
private static readonly AzureKeyCredential credentials = new AzureKeyCredential("replace-with-your-key-here");
private static readonly Uri endpoint = new Uri("replace-with-your-endpoint-here");
// Example method for extracting information from healthcare-related text
static async Task healthExample(TextAnalyticsClient client)
{
string document = "Prescribed 100mg ibuprofen, taken twice daily.";
List<string> batchInput = new List<string>()
{
document
};
AnalyzeHealthcareEntitiesOperation healthOperation = await client.StartAnalyzeHealthcareEntitiesAsync(batchInput);
await healthOperation.WaitForCompletionAsync();
await foreach (AnalyzeHealthcareEntitiesResultCollection documentsInPage in healthOperation.Value)
{
Console.WriteLine($"Results of Azure Text Analytics for health async model, version: \"{documentsInPage.ModelVersion}\"");
Console.WriteLine("");
foreach (AnalyzeHealthcareEntitiesResult entitiesInDoc in documentsInPage)
{
if (!entitiesInDoc.HasError)
{
foreach (var entity in entitiesInDoc.Entities)
{
// view recognized healthcare entities
Console.WriteLine($" Entity: {entity.Text}");
Console.WriteLine($" Category: {entity.Category}");
Console.WriteLine($" Offset: {entity.Offset}");
Console.WriteLine($" Length: {entity.Length}");
Console.WriteLine($" NormalizedText: {entity.NormalizedText}");
}
Console.WriteLine($" Found {entitiesInDoc.EntityRelations.Count} relations in the current document:");
Console.WriteLine("");
// view recognized healthcare relations
foreach (HealthcareEntityRelation relations in entitiesInDoc.EntityRelations)
{
Console.WriteLine($" Relation: {relations.RelationType}");
Console.WriteLine($" For this relation there are {relations.Roles.Count} roles");
// view relation roles
foreach (HealthcareEntityRelationRole role in relations.Roles)
{
Console.WriteLine($" Role Name: {role.Name}");
Console.WriteLine($" Associated Entity Text: {role.Entity.Text}");
Console.WriteLine($" Associated Entity Category: {role.Entity.Category}");
Console.WriteLine("");
}
Console.WriteLine("");
}
}
else
{
Console.WriteLine(" Error!");
Console.WriteLine($" Document error code: {entitiesInDoc.Error.ErrorCode}.");
Console.WriteLine($" Message: {entitiesInDoc.Error.Message}");
}
Console.WriteLine("");
}
}
}
static async Task Main(string[] args)
{
var client = new TextAnalyticsClient(endpoint, credentials);
await healthExample(client);
}
}
}
Output
Results of Azure Text Analytics for health async model, version: "2022-03-01"
Entity: 100mg
Category: Dosage
Offset: 11
Length: 5
NormalizedText:
Entity: ibuprofen
Category: MedicationName
Offset: 17
Length: 9
NormalizedText: ibuprofen
Entity: twice daily
Category: Frequency
Offset: 34
Length: 11
NormalizedText:
Found 2 relations in the current document:
Relation: DosageOfMedication
For this relation there are 2 roles
Role Name: Dosage
Associated Entity Text: 100mg
Associated Entity Category: Dosage
Role Name: Medication
Associated Entity Text: ibuprofen
Associated Entity Category: MedicationName
Relation: FrequencyOfMedication
For this relation there are 2 roles
Role Name: Medication
Associated Entity Text: ibuprofen
Associated Entity Category: MedicationName
Role Name: Frequency
Associated Entity Text: twice daily
Associated Entity Category: Frequency
Tip
Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. Learn more on how to use FHIR structuring in your API call.
Clean up resources
If you want to clean up and remove a Cognitive Services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
Reference documentation | Additional samples | Package (Maven) | Library source code
Use this quickstart to create a Text Analytics for health application with the client library for Java. In the following example, you will create a Java application that can identify medical entities, relations, and assertions that appear in text.
Prerequisites
- Azure subscription - Create one for free
- Java Development Kit (JDK) with version 8 or above
- Once you have your Azure subscription, create a Language resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource.
- You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
- You can use the free pricing tier (
Free F0) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to theStandard Spricing tier for production. You can also start with theStandard Spricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit Language Service Pricing.
Setting up
Add the client library
Create a Maven project in your preferred IDE or development environment. Then add the following dependency to your project's pom.xml file. You can find the implementation syntax for other build tools online.
<dependencies>
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-textanalytics</artifactId>
<version>5.2.0</version>
</dependency>
</dependencies>
Code example
Create a Java file named EntityLinking.java. Open the file and copy the below code. Remember to replace the key variable with the key for your resource, and replace the endpoint variable with the endpoint for your resource. Then run the code.
Important
Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.
Important
Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Cognitive Services security article for more information.
import com.azure.core.credential.AzureKeyCredential;
import com.azure.ai.textanalytics.models.*;
import com.azure.ai.textanalytics.TextAnalyticsClientBuilder;
import com.azure.ai.textanalytics.TextAnalyticsClient;
import java.util.List;
import java.util.Arrays;
import com.azure.core.util.Context;
import com.azure.core.util.polling.SyncPoller;
import com.azure.ai.textanalytics.util.*;
public class Example {
private static String KEY = "replace-with-your-key-here";
private static String ENDPOINT = "replace-with-your-endpoint-here";
public static void main(String[] args) {
TextAnalyticsClient client = authenticateClient(KEY, ENDPOINT);
healthExample(client);
}
// Method to authenticate the client object with your key and endpoint
static TextAnalyticsClient authenticateClient(String key, String endpoint) {
return new TextAnalyticsClientBuilder()
.credential(new AzureKeyCredential(key))
.endpoint(endpoint)
.buildClient();
}
// Example method for extracting information from healthcare-related text
static void healthExample(TextAnalyticsClient client){
List<TextDocumentInput> documents = Arrays.asList(
new TextDocumentInput("0",
"Prescribed 100mg ibuprofen, taken twice daily."));
AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions().setIncludeStatistics(true);
SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
syncPoller = client.beginAnalyzeHealthcareEntities(documents, options, Context.NONE);
System.out.printf("Poller status: %s.%n", syncPoller.poll().getStatus());
syncPoller.waitForCompletion();
// Task operation statistics
AnalyzeHealthcareEntitiesOperationDetail operationResult = syncPoller.poll().getValue();
System.out.printf("Operation created time: %s, expiration time: %s.%n",
operationResult.getCreatedAt(), operationResult.getExpiresAt());
System.out.printf("Poller status: %s.%n", syncPoller.poll().getStatus());
for (AnalyzeHealthcareEntitiesResultCollection resultCollection : syncPoller.getFinalResult()) {
// Model version
System.out.printf(
"Results of Azure Text Analytics for health entities\" Model, version: %s%n",
resultCollection.getModelVersion());
for (AnalyzeHealthcareEntitiesResult healthcareEntitiesResult : resultCollection) {
System.out.println("Document ID = " + healthcareEntitiesResult.getId());
System.out.println("Document entities: ");
// Recognized healthcare entities
for (HealthcareEntity entity : healthcareEntitiesResult.getEntities()) {
System.out.printf(
"\tText: %s, normalized name: %s, category: %s, subcategory: %s, confidence score: %f.%n",
entity.getText(), entity.getNormalizedText(), entity.getCategory(),
entity.getSubcategory(), entity.getConfidenceScore());
}
// Recognized healthcare entity relation groups
for (HealthcareEntityRelation entityRelation : healthcareEntitiesResult.getEntityRelations()) {
System.out.printf("Relation type: %s.%n", entityRelation.getRelationType());
for (HealthcareEntityRelationRole role : entityRelation.getRoles()) {
HealthcareEntity entity = role.getEntity();
System.out.printf("\tEntity text: %s, category: %s, role: %s.%n",
entity.getText(), entity.getCategory(), role.getName());
}
}
}
}
}
}
Output
Poller status: IN_PROGRESS.
Operation created time: 2022-09-15T19:06:11Z, expiration time: 2022-09-16T19:06:11Z.
Poller status: SUCCESSFULLY_COMPLETED.
Results of Azure Text Analytics for health entities" Model, version: 2022-03-01
Document ID = 0
Document entities:
Text: 100mg, normalized name: null, category: Dosage, subcategory: null, confidence score: 0.980000.
Text: ibuprofen, normalized name: ibuprofen, category: MedicationName, subcategory: null, confidence score: 1.000000.
Text: twice daily, normalized name: null, category: Frequency, subcategory: null, confidence score: 1.000000.
Relation type: DosageOfMedication.
Entity text: 100mg, category: Dosage, role: Dosage.
Entity text: ibuprofen, category: MedicationName, role: Medication.
Relation type: FrequencyOfMedication.
Entity text: ibuprofen, category: MedicationName, role: Medication.
Entity text: twice daily, category: Frequency, role: Frequency.
Tip
Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. Learn more on how to use FHIR structuring in your API call.
Clean up resources
If you want to clean up and remove a Cognitive Services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
Reference documentation | Additional samples | Package (npm) | Library source code
Use this quickstart to create a Text Analytics for health application with the client library for Node.js. In the following example, you will create a JavaScript application that can identify medical entities, relations, and assertions that appear in text.
Prerequisites
- Azure subscription - Create one for free
- Node.js v14 LTS or later
- Once you have your Azure subscription, create a Language resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource.
- You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
- You can use the free pricing tier (
Free F0) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to theStandard Spricing tier for production. You can also start with theStandard Spricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit Language Service Pricing.
Setting up
Create a new Node.js application
In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.
mkdir myapp
cd myapp
Run the npm init command to create a node application with a package.json file.
npm init
Install the client library
Install the npm package:
npm install --save @azure/ai-text-analytics@5.1.0
Code example
Open the file and copy the below code. Remember to replace the key variable with the key for your resource, and replace the endpoint variable with the endpoint for your resource. Then run the code.
Important
Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.
Important
Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Cognitive Services security article for more information.
"use strict";
const { TextAnalyticsClient, AzureKeyCredential } = require("@azure/ai-text-analytics");
const key = '<paste-your-key-here>';
const endpoint = '<paste-your-endpoint-here>';
// Authenticate the client with your key and endpoint.
const textAnalyticsClient = new TextAnalyticsClient(endpoint, new AzureKeyCredential(key));
// Example method for extracting information from healthcare-related text.
async function healthExample(client) {
console.log("== Recognize Healthcare Entities Sample ==");
const documents = [
"Prescribed 100mg ibuprofen, taken twice daily."
];
const poller = await client.beginAnalyzeHealthcareEntities(documents, "en", {
includeStatistics: true
});
poller.onProgress(() => {
console.log(
`Last time the operation was updated was on: ${poller.getOperationState().lastModifiedOn}`
);
});
console.log(
`The analyze healthcare entities operation was created on ${poller.getOperationState().createdOn
}`
);
console.log(
`The analyze healthcare entities operation results will expire on ${poller.getOperationState().expiresOn
}`
);
const results = await poller.pollUntilDone();
for await (const result of results) {
console.log(`- Document ${result.id}`);
if (!result.error) {
console.log("\tRecognized Entities:");
for (const entity of result.entities) {
console.log(`\t- Entity "${entity.text}" of type ${entity.category}`);
}
if (result.entityRelations && (result.entityRelations.length > 0)) {
console.log(`\tRecognized relations between entities:`);
for (const relation of result.entityRelations) {
console.log(
`\t\t- Relation of type ${relation.relationType} found between the following entities:`
);
for (const role of relation.roles) {
console.log(`\t\t\t- "${role.entity.text}" with the role ${role.name}`);
}
}
}
} else console.error("\tError:", result.error);
}
}
healthExample(textAnalyticsClient).catch((err) => {
console.error("The sample encountered an error:", err);
});
Output
- Document 0
Recognized Entities:
- Entity "100mg" of type Dosage
- Entity "ibuprofen" of type MedicationName
- Entity "twice daily" of type Frequency
Recognized relations between entities:
- Relation of type DosageOfMedication found between the following entities:
- "100mg" with the role Dosage
- "ibuprofen" with the role Medication
- Relation of type FrequencyOfMedication found between the following entities:
- "ibuprofen" with the role Medication
- "twice daily" with the role Frequency
Tip
Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. Learn more on how to use FHIR structuring in your API call.
Clean up resources
If you want to clean up and remove a Cognitive Services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
Reference documentation | Additional samples | Package (PyPi) | Library source code
Use this quickstart to create a Text Analytics for health application with the client library for Python. In the following example, you will create a Python application that can identify medical entities, relations, and assertions that appear in text.
Prerequisites
- Azure subscription - Create one for free
- Python 3.7 or later
- Once you have your Azure subscription, create a Language resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource.
- You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
- You can use the free pricing tier (
Free F0) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to theStandard Spricing tier for production. You can also start with theStandard Spricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit Language Service Pricing.
Setting up
Install the client library
After installing Python, you can install the client library with:
pip install azure-ai-textanalytics==5.2.0
Code example
Create a new Python file and copy the below code. Remember to replace the key variable with the key for your resource, and replace the endpoint variable with the endpoint for your resource. Then run the code.
Important
Go to the Azure portal. If the Language resource you created in the Prerequisites section deployed successfully, click the Go to Resource button under Next Steps. You can find your key and endpoint by navigating to your resource's Keys and Endpoint page, under Resource Management.
Important
Remember to remove the key from your code when you're done, and never post it publicly. For production, use a secure way of storing and accessing your credentials like Azure Key Vault. See the Cognitive Services security article for more information.
key = "paste-your-key-here"
endpoint = "paste-your-endpoint-here"
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
# Authenticate the client using your key and endpoint
def authenticate_client():
ta_credential = AzureKeyCredential(key)
text_analytics_client = TextAnalyticsClient(
endpoint=endpoint,
credential=ta_credential)
return text_analytics_client
client = authenticate_client()
# Example function for extracting information from healthcare-related text
def health_example(client):
documents = [
"""
Patient needs to take 50 mg of ibuprofen.
"""
]
poller = client.begin_analyze_healthcare_entities(documents)
result = poller.result()
docs = [doc for doc in result if not doc.is_error]
for idx, doc in enumerate(docs):
for entity in doc.entities:
print("Entity: {}".format(entity.text))
print("...Normalized Text: {}".format(entity.normalized_text))
print("...Category: {}".format(entity.category))
print("...Subcategory: {}".format(entity.subcategory))
print("...Offset: {}".format(entity.offset))
print("...Confidence score: {}".format(entity.confidence_score))
for relation in doc.entity_relations:
print("Relation of type: {} has the following roles".format(relation.relation_type))
for role in relation.roles:
print("...Role '{}' with entity '{}'".format(role.name, role.entity.text))
print("------------------------------------------")
health_example(client)
Output
Entity: 50 mg
...Normalized Text: None
...Category: Dosage
...Subcategory: None
...Offset: 31
...Confidence score: 1.0
Entity: ibuprofen
...Normalized Text: ibuprofen
...Category: MedicationName
...Subcategory: None
...Offset: 40
...Confidence score: 1.0
Relation of type: DosageOfMedication has the following roles
...Role 'Dosage' with entity '50 mg'
...Role 'Medication' with entity 'ibuprofen'
Tip
Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. Learn more on how to use FHIR structuring in your API call.
Clean up resources
If you want to clean up and remove a Cognitive Services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.
Use this quickstart to send language detection requests using the REST API. In the following example, you will use cURL to identify medical entities, relations, and assertions that appear in text.
Prerequisites
- The current version of cURL
- An Azure subscription - create one for free
- Once you have your Azure subscription, create a Language resource in the Azure portal to get your key and endpoint. After it deploys, click Go to resource.
- You will need the key and endpoint from the resource you create to connect your application to the API. You'll paste your key and endpoint into the code below later in the quickstart.
- You can use the free pricing tier (
Free F0) to try the service (providing 5000 text records - 1000 characters each) and upgrade later to theStandard Spricing tier for production. You can also start with theStandard Spricing tier, receiving the same initial quota for free (5000 text records) before getting charged. For more information on pricing, visit Language Service Pricing.
Note
- The following BASH examples use the
\line continuation character. If your console or terminal uses a different line continuation character, use that character. - You can find language specific samples on GitHub.
- Go to the Azure portal and find the key and endpoint for the Language resource you created in the prerequisites. They will be located on the resource's key and endpoint page, under resource management. Then replace the strings in the code below with your key and endpoint. To call the API, you need the following information:
| parameter | Description |
|---|---|
-X POST <endpoint> |
Specifies your endpoint for accessing the API. |
-H Content-Type: application/json |
The content type for sending JSON data. |
-H "Ocp-Apim-Subscription-Key:<key> |
Specifies the key for accessing the API. |
-d <documents> |
The JSON containing the documents you want to send. |
The following cURL commands are executed from a BASH shell. Edit these commands with your own resource name, resource key, and JSON values.
Text Analytics for health
- Copy the command into a text editor.
- Make the following changes in the command where needed:
- Replace the value
<your-language-resource-key>with your key. - Replace the first part of the request URL
<your-language-resource-endpoint>with your endpoint URL.
- Replace the value
- Open a command prompt window.
- Paste the command from the text editor into the command prompt window, and then run the command.
curl -i -X POST https://your-Language-endpoint-here/language/analyze-text/jobs?api-version=2022-05-15-preview \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: your-Language-key-here" \
-d '{"analysisInput":{"documents": [{"text": "The doctor prescried 200mg Ibuprofen.","language": "en","id": "1"}]},"tasks":[{"taskId": "analyze 1","kind": "Healthcare","parameters": {"fhirVersion": "4.0.1"}}]}'
Get the operation-location from the response header. The value will look similar to the following URL:
https://your-resource.cognitiveservices.azure.com/language/analyze-text/jobs/{JOB-ID}?api-version=2022-05-15-preview
To get the results of the request, use the following cURL command. Be sure to replace {JOB-ID} with the numerical ID value you received from the previous operation-location response header:
curl -X GET https://your-Language-endpoint-here/language/analyze-text/jobs/{JOB-ID}?api-version=2022-05-15-preview \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: your-Language-key-here"
JSON response
{
"jobId": "{JOB-ID}",
"lastUpdatedDateTime": "2022-06-27T22:04:39Z",
"createdDateTime": "2022-06-27T22:04:38Z",
"expirationDateTime": "2022-06-28T22:04:38Z",
"status": "succeeded",
"errors": [],
"tasks": {
"completed": 1,
"failed": 0,
"inProgress": 0,
"total": 1,
"items": [
{
"kind": "HealthcareLROResults",
"lastUpdateDateTime": "2022-06-27T22:04:39.7086762Z",
"status": "succeeded",
"results": {
"documents": [
{
"id": "1",
"entities": [
{
"offset": 4,
"length": 6,
"text": "doctor",
"category": "HealthcareProfession",
"confidenceScore": 0.76
},
{
"offset": 21,
"length": 5,
"text": "200mg",
"category": "Dosage",
"confidenceScore": 0.99
},
{
"offset": 27,
"length": 9,
"text": "Ibuprofen",
"category": "MedicationName",
"confidenceScore": 1.0,
"name": "ibuprofen",
"links": [
{
"dataSource": "UMLS",
"id": "C0020740"
},
{
"dataSource": "AOD",
"id": "0000019879"
},
{
"dataSource": "ATC",
"id": "M01AE01"
},
{
"dataSource": "CCPSS",
"id": "0046165"
},
{
"dataSource": "CHV",
"id": "0000006519"
},
{
"dataSource": "CSP",
"id": "2270-2077"
},
{
"dataSource": "DRUGBANK",
"id": "DB01050"
},
{
"dataSource": "GS",
"id": "1611"
},
{
"dataSource": "LCH_NW",
"id": "sh97005926"
},
{
"dataSource": "LNC",
"id": "LP16165-0"
},
{
"dataSource": "MEDCIN",
"id": "40458"
},
{
"dataSource": "MMSL",
"id": "d00015"
},
{
"dataSource": "MSH",
"id": "D007052"
},
{
"dataSource": "MTHSPL",
"id": "WK2XYI10QM"
},
{
"dataSource": "NCI",
"id": "C561"
},
{
"dataSource": "NCI_CTRP",
"id": "C561"
},
{
"dataSource": "NCI_DCP",
"id": "00803"
},
{
"dataSource": "NCI_DTP",
"id": "NSC0256857"
},
{
"dataSource": "NCI_FDA",
"id": "WK2XYI10QM"
},
{
"dataSource": "NCI_NCI-GLOSS",
"id": "CDR0000613511"
},
{
"dataSource": "NDDF",
"id": "002377"
},
{
"dataSource": "PDQ",
"id": "CDR0000040475"
},
{
"dataSource": "RCD",
"id": "x02MO"
},
{
"dataSource": "RXNORM",
"id": "5640"
},
{
"dataSource": "SNM",
"id": "E-7772"
},
{
"dataSource": "SNMI",
"id": "C-603C0"
},
{
"dataSource": "SNOMEDCT_US",
"id": "387207008"
},
{
"dataSource": "USP",
"id": "m39860"
},
{
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}
Tip
Fast Healthcare Interoperability Resources (FHIR) structuring is available for preview using the Language REST API. The client libraries are not currently supported. Learn more on how to use FHIR structuring in your API call.
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