Cloud Healthcare API . projects . locations . services . nlp

analyzeEntities(nlpService, body=None, x__xgafv=None)

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.
  "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": [ # entity_mentions 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.it
        { # 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.
    },
  ],
  "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.
    },
  ],
}

close()

Close httplib2 connections.