<html><body>
<style>

body, h1, h2, h3, div, span, p, pre, a {
  margin: 0;
  padding: 0;
  border: 0;
  font-weight: inherit;
  font-style: inherit;
  font-size: 100%;
  font-family: inherit;
  vertical-align: baseline;
}

body {
  font-size: 13px;
  padding: 1em;
}

h1 {
  font-size: 26px;
  margin-bottom: 1em;
}

h2 {
  font-size: 24px;
  margin-bottom: 1em;
}

h3 {
  font-size: 20px;
  margin-bottom: 1em;
  margin-top: 1em;
}

pre, code {
  line-height: 1.5;
  font-family: Monaco, 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Lucida Console', monospace;
}

pre {
  margin-top: 0.5em;
}

h1, h2, h3, p {
  font-family: Arial, sans serif;
}

h1, h2, h3 {
  border-bottom: solid #CCC 1px;
}

.toc_element {
  margin-top: 0.5em;
}

.firstline {
  margin-left: 2 em;
}

.method  {
  margin-top: 1em;
  border: solid 1px #CCC;
  padding: 1em;
  background: #EEE;
}

.details {
  font-weight: bold;
  font-size: 14px;
}

</style>

<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: &quot;projects/{project_id}/locations/{location_id}/services/nlp&quot;. (required)
  body: object, The request body.
    The object takes the form of:

{ # The request to analyze healthcare entities in a document.
  &quot;documentContent&quot;: &quot;A String&quot;, # document_content is a document to be annotated.
  &quot;licensedVocabularies&quot;: [ # A list of licensed vocabularies to use in the request, in addition to the default unlicensed vocabularies.
    &quot;A String&quot;,
  ],
}

  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.
  &quot;entities&quot;: [ # 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.
      &quot;entityId&quot;: &quot;A String&quot;, # entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, &quot;UMLS/C0000970&quot;.
      &quot;preferredTerm&quot;: &quot;A String&quot;, # preferred_term is the preferred term for this concept. For example, &quot;Acetaminophen&quot;. For ad hoc entities formed by normalization, this is the most popular unnormalized string.
      &quot;vocabularyCodes&quot;: [ # 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, &quot;RXNORM/A10334543&quot;.
        &quot;A String&quot;,
      ],
    },
  ],
  &quot;entityMentions&quot;: [ # entity_mentions contains all the annotated medical entities that were mentioned in the provided document.
    { # An entity mention in the document.
      &quot;certaintyAssessment&quot;: { # 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
        &quot;confidence&quot;: 3.14, # The model&#x27;s confidence in this feature annotation. A number between 0 and 1.
        &quot;value&quot;: &quot;A String&quot;, # The value of this feature annotation. Its range depends on the type of the feature.
      },
      &quot;confidence&quot;: 3.14, # The model&#x27;s confidence in this entity mention annotation. A number between 0 and 1.
      &quot;linkedEntities&quot;: [ # linked_entities are candidate ontological concepts that this entity mention may refer to. They are sorted by decreasing confidence.it
        { # EntityMentions can be linked to multiple entities using a LinkedEntity message lets us add other fields, e.g. confidence.
          &quot;entityId&quot;: &quot;A String&quot;, # 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, &quot;UMLS/C0000970&quot;. This also supports ad hoc entities, which are formed by normalizing entity mention content.
        },
      ],
      &quot;mentionId&quot;: &quot;A String&quot;, # mention_id uniquely identifies each entity mention in a single response.
      &quot;subject&quot;: { # A feature of an entity mention. # The subject this entity mention relates to. Its value is one of: PATIENT, FAMILY_MEMBER, OTHER
        &quot;confidence&quot;: 3.14, # The model&#x27;s confidence in this feature annotation. A number between 0 and 1.
        &quot;value&quot;: &quot;A String&quot;, # The value of this feature annotation. Its range depends on the type of the feature.
      },
      &quot;temporalAssessment&quot;: { # 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
        &quot;confidence&quot;: 3.14, # The model&#x27;s confidence in this feature annotation. A number between 0 and 1.
        &quot;value&quot;: &quot;A String&quot;, # The value of this feature annotation. Its range depends on the type of the feature.
      },
      &quot;text&quot;: { # A span of text in the provided document. # text is the location of the entity mention in the document.
        &quot;beginOffset&quot;: 42, # The unicode codepoint index of the beginning of this span.
        &quot;content&quot;: &quot;A String&quot;, # The original text contained in this span.
      },
      &quot;type&quot;: &quot;A String&quot;, # 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.
    },
  ],
  &quot;relationships&quot;: [ # 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.
      &quot;confidence&quot;: 3.14, # The model&#x27;s confidence in this annotation. A number between 0 and 1.
      &quot;objectId&quot;: &quot;A String&quot;, # object_id is the id of the object entity mention.
      &quot;subjectId&quot;: &quot;A String&quot;, # 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>

</body></html>