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. Amazon Comprehend Medical only detects medical entities in English language texts.
Parameters:
Raises:
-
InternalServerException
–
An internal server error occurred. Retry your request.
-
InvalidEncodingException
–
The input text was not in valid UTF-8 character encoding. Check your text then retry your request.
-
InvalidRequestException
–
The request that you made is invalid. Check your request to determine why it's invalid and then retry the request.
-
ServiceUnavailableException
–
The Amazon Comprehend Medical service is temporarily unavailable. Please wait and then retry your request.
-
TextSizeLimitExceededException
–
The size of the text you submitted exceeds the size limit. Reduce the size of the text or use a smaller document and then retry your request.
-
TooManyRequestsException
–
You have made too many requests within a short period of time. Wait for a short time and then try your request again. Contact customer support for more information about a service limit increase.
-
UnknownServiceError
–
The service returned an error code this client does not model.
Source code in docs_src/capo_comprehendmedical/_services/comprehend_medical.py
| def infer_icd10_cm(
self,
text: "capo_comprehendmedical.types.ontology_linking_bounded_length_string.OntologyLinkingBoundedLengthString",
*,
config_overrides: Optional[ComprehendMedicalClientConfig] = None,
) -> "capo_comprehendmedical.types.infer_icd10_cm_response.InferICD10CMResponse":
"""<p>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. Amazon Comprehend Medical only detects medical entities in English language texts. </p>
Args:
text: <p>The input text used for analysis.</p>
Raises:
capo_comprehendmedical.errors.internal_server_exception.InternalServerException: <p> An internal server error occurred. Retry your request. </p>
capo_comprehendmedical.errors.invalid_encoding_exception.InvalidEncodingException: <p> The input text was not in valid UTF-8 character encoding. Check your text then retry your request.</p>
capo_comprehendmedical.errors.invalid_request_exception.InvalidRequestException: <p> The request that you made is invalid. Check your request to determine why it's invalid and then retry the request.</p>
capo_comprehendmedical.errors.service_unavailable_exception.ServiceUnavailableException: <p> The Amazon Comprehend Medical service is temporarily unavailable. Please wait and then retry your request. </p>
capo_comprehendmedical.errors.text_size_limit_exceeded_exception.TextSizeLimitExceededException: <p> The size of the text you submitted exceeds the size limit. Reduce the size of the text or use a smaller document and then retry your request. </p>
capo_comprehendmedical.errors.too_many_requests_exception.TooManyRequestsException: <p> You have made too many requests within a short period of time. Wait for a short time and then try your request again. Contact customer support for more information about a service limit increase. </p>
capo_comprehendmedical.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_comprehendmedical.types.infer_icd10_cm_request.InferICD10CMRequest]",
) -> OperationResponse[
"capo_comprehendmedical.types.infer_icd10_cm_response.InferICD10CMResponse"
]:
import capo_comprehendmedical._operations.comprehend_medical_20181030.infer_icd10_cm
output, http_response = (
capo_comprehendmedical._operations.comprehend_medical_20181030.infer_icd10_cm.infer_icd10_cm(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("InferICD10CM")
)
input_: capo_comprehendmedical.types.infer_icd10_cm_request.InferICD10CMRequest = {
"text": text
}
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|