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<h1><a href="healthcare_v1.html">Cloud Healthcare API</a> . <a href="healthcare_v1.projects.html">projects</a> . <a href="healthcare_v1.projects.locations.html">locations</a> . <a href="healthcare_v1.projects.locations.services.html">services</a> . <a href="healthcare_v1.projects.locations.services.nlp.html">nlp</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
<code><a href="#analyzeEntities">analyzeEntities(nlpService, body=None, x__xgafv=None)</a></code></p>
<p class="firstline">Analyze heathcare entity in a document. Its response includes the recognized entity mentions and the relationships between them. AnalyzeEntities uses context aware models to detect entities.</p>
<p class="toc_element">
<code><a href="#close">close()</a></code></p>
<p class="firstline">Close httplib2 connections.</p>
<h3>Method Details</h3>
<div class="method">
<code class="details" id="analyzeEntities">analyzeEntities(nlpService, body=None, x__xgafv=None)</code>
<pre>Analyze heathcare entity in a document. Its response includes the recognized entity mentions and the relationships between them. AnalyzeEntities uses context aware models to detect entities.
Args:
nlpService: string, The resource name of the service of the form: "projects/{project_id}/locations/{location_id}/services/nlp". (required)
body: object, The request body.
The object takes the form of:
{ # The request to analyze healthcare entities in a document.
"alternativeOutputFormat": "A String", # Optional. Alternative output format to be generated based on the results of analysis.
"documentContent": "A String", # document_content is a document to be annotated.
"licensedVocabularies": [ # A list of licensed vocabularies to use in the request, in addition to the default unlicensed vocabularies.
"A String",
],
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Includes recognized entity mentions and relationships between them.
"entities": [ # The union of all the candidate entities that the entity_mentions in this response could link to. These are UMLS concepts or normalized mention content.
{ # The candidate entities that an entity mention could link to.
"entityId": "A String", # entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, "UMLS/C0000970".
"preferredTerm": "A String", # preferred_term is the preferred term for this concept. For example, "Acetaminophen". For ad hoc entities formed by normalization, this is the most popular unnormalized string.
"vocabularyCodes": [ # Vocabulary codes are first-class fields and differentiated from the concept unique identifier (entity_id). vocabulary_codes contains the representation of this concept in particular vocabularies, such as ICD-10, SNOMED-CT and RxNORM. These are prefixed by the name of the vocabulary, followed by the unique code within that vocabulary. For example, "RXNORM/A10334543".
"A String",
],
},
],
"entityMentions": [ # The `entity_mentions` field contains all the annotated medical entities that were mentioned in the provided document.
{ # An entity mention in the document.
"certaintyAssessment": { # A feature of an entity mention. # The certainty assessment of the entity mention. Its value is one of: LIKELY, SOMEWHAT_LIKELY, UNCERTAIN, SOMEWHAT_UNLIKELY, UNLIKELY, CONDITIONAL
"confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1.
"value": "A String", # The value of this feature annotation. Its range depends on the type of the feature.
},
"confidence": 3.14, # The model's confidence in this entity mention annotation. A number between 0 and 1.
"linkedEntities": [ # linked_entities are candidate ontological concepts that this entity mention may refer to. They are sorted by decreasing confidence.
{ # EntityMentions can be linked to multiple entities using a LinkedEntity message lets us add other fields, e.g. confidence.
"entityId": "A String", # entity_id is a concept unique identifier. These are prefixed by a string that identifies the entity coding system, followed by the unique identifier within that system. For example, "UMLS/C0000970". This also supports ad hoc entities, which are formed by normalizing entity mention content.
},
],
"mentionId": "A String", # mention_id uniquely identifies each entity mention in a single response.
"subject": { # A feature of an entity mention. # The subject this entity mention relates to. Its value is one of: PATIENT, FAMILY_MEMBER, OTHER
"confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1.
"value": "A String", # The value of this feature annotation. Its range depends on the type of the feature.
},
"temporalAssessment": { # A feature of an entity mention. # How this entity mention relates to the subject temporally. Its value is one of: CURRENT, CLINICAL_HISTORY, FAMILY_HISTORY, UPCOMING, ALLERGY
"confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1.
"value": "A String", # The value of this feature annotation. Its range depends on the type of the feature.
},
"text": { # A span of text in the provided document. # text is the location of the entity mention in the document.
"beginOffset": 42, # The unicode codepoint index of the beginning of this span.
"content": "A String", # The original text contained in this span.
},
"type": "A String", # The semantic type of the entity: UNKNOWN_ENTITY_TYPE, ALONE, ANATOMICAL_STRUCTURE, ASSISTED_LIVING, BF_RESULT, BM_RESULT, BM_UNIT, BM_VALUE, BODY_FUNCTION, BODY_MEASUREMENT, COMPLIANT, DOESNOT_FOLLOWUP, FAMILY, FOLLOWSUP, LABORATORY_DATA, LAB_RESULT, LAB_UNIT, LAB_VALUE, MEDICAL_DEVICE, MEDICINE, MED_DOSE, MED_DURATION, MED_FORM, MED_FREQUENCY, MED_ROUTE, MED_STATUS, MED_STRENGTH, MED_TOTALDOSE, MED_UNIT, NON_COMPLIANT, OTHER_LIVINGSTATUS, PROBLEM, PROCEDURE, PROCEDURE_RESULT, PROC_METHOD, REASON_FOR_NONCOMPLIANCE, SEVERITY, SUBSTANCE_ABUSE, UNCLEAR_FOLLOWUP.
},
],
"fhirBundle": "A String", # The FHIR bundle ([`R4`](http://hl7.org/fhir/R4/bundle.html)) that includes all the entities, the entity mentions, and the relationships in JSON format.
"relationships": [ # relationships contains all the binary relationships that were identified between entity mentions within the provided document.
{ # Defines directed relationship from one entity mention to another.
"confidence": 3.14, # The model's confidence in this annotation. A number between 0 and 1.
"objectId": "A String", # object_id is the id of the object entity mention.
"subjectId": "A String", # subject_id is the id of the subject entity mention.
},
],
}</pre>
</div>
<div class="method">
<code class="details" id="close">close()</code>
<pre>Close httplib2 connections.</pre>
</div>
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