comprehendmedical: AWS Comprehend Medical

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

AWS Comprehend Medical

Description

Amazon Comprehend Medical extracts structured information from unstructured clinical text. Use these actions to gain insight in your documents. Amazon Comprehend Medical only detects entities in English language texts. Amazon Comprehend Medical places limits on the sizes of files allowed for different API operations. To learn more, see Guidelines and quotas in the Amazon Comprehend Medical Developer Guide.

Usage

comprehendmedical(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments

config	Optional configuration of credentials, endpoint, and/or region. credentials: creds: access_key_id: AWS access key ID secret_access_key: AWS secret access key session_token: AWS temporary session token profile: The name of a profile to use. If not given, then the default profile is used. anonymous: Set anonymous credentials. endpoint: The complete URL to use for the constructed client. region: The AWS Region used in instantiating the client. close_connection: Immediately close all HTTP connections. timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds. s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠. sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html
credentials	Optional credentials shorthand for the config parameter creds: access_key_id: AWS access key ID secret_access_key: AWS secret access key session_token: AWS temporary session token profile: The name of a profile to use. If not given, then the default profile is used. anonymous: Set anonymous credentials.
endpoint	Optional shorthand for complete URL to use for the constructed client.
region	Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- comprehendmedical(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

describe_entities_detection_v2_job	Gets the properties associated with a medical entities detection job
describe_icd10cm_inference_job	Gets the properties associated with an InferICD10CM job
describe_phi_detection_job	Gets the properties associated with a protected health information (PHI) detection job
describe_rx_norm_inference_job	Gets the properties associated with an InferRxNorm job
describe_snomedct_inference_job	Gets the properties associated with an InferSNOMEDCT job
detect_entities	The DetectEntities operation is deprecated
detect_entities_v2	Inspects the clinical text for a variety of medical entities and returns specific information about them such as entity category, location, and confidence score on that information
detect_phi	Inspects the clinical text for protected health information (PHI) entities and returns the entity category, location, and confidence score for each entity
infer_icd10cm	InferICD10CM detects medical conditions as entities listed in a patient record and links those entities to normalized concept identifiers in the ICD-10-CM knowledge base from the Centers for Disease Control
infer_rx_norm	InferRxNorm detects medications as entities listed in a patient record and links to the normalized concept identifiers in the RxNorm database from the National Library of Medicine
infer_snomedct	InferSNOMEDCT detects possible medical concepts as entities and links them to codes from the Systematized Nomenclature of Medicine, Clinical Terms (SNOMED-CT) ontology
list_entities_detection_v2_jobs	Gets a list of medical entity detection jobs that you have submitted
list_icd10cm_inference_jobs	Gets a list of InferICD10CM jobs that you have submitted
list_phi_detection_jobs	Gets a list of protected health information (PHI) detection jobs you have submitted
list_rx_norm_inference_jobs	Gets a list of InferRxNorm jobs that you have submitted
list_snomedct_inference_jobs	Gets a list of InferSNOMEDCT jobs a user has submitted
start_entities_detection_v2_job	Starts an asynchronous medical entity detection job for a collection of documents
start_icd10cm_inference_job	Starts an asynchronous job to detect medical conditions and link them to the ICD-10-CM ontology
start_phi_detection_job	Starts an asynchronous job to detect protected health information (PHI)
start_rx_norm_inference_job	Starts an asynchronous job to detect medication entities and link them to the RxNorm ontology
start_snomedct_inference_job	Starts an asynchronous job to detect medical concepts and link them to the SNOMED-CT ontology
stop_entities_detection_v2_job	Stops a medical entities detection job in progress
stop_icd10cm_inference_job	Stops an InferICD10CM inference job in progress
stop_phi_detection_job	Stops a protected health information (PHI) detection job in progress
stop_rx_norm_inference_job	Stops an InferRxNorm inference job in progress
stop_snomedct_inference_job	Stops an InferSNOMEDCT inference job in progress

Examples

## Not run: 
svc <- comprehendmedical()
svc$describe_entities_detection_v2_job(
  Foo = 123
)

## End(Not run)

paws.machine.learning documentation built on April 3, 2025, 8:41 p.m.