Package google.cloud.healthcare.v1beta1.nlp

Index

NlpService (interface)
AnalyzeEntitiesRequest (message)
AnalyzeEntitiesRequest.AlternativeOutputFormat (enum)
AnalyzeEntitiesRequest.LicensedVocabulary (enum)
AnalyzeEntitiesResponse (message)
Entity (message)
EntityMention (message)
EntityMention.Feature (message)
EntityMention.LinkedEntity (message)
EntityMentionRelationship (message)
TextSpan (message)

NlpService

A service to analyzing healthcare documents.

AnalyzeEntities

AnalyzeEntities
`rpc AnalyzeEntities(AnalyzeEntitiesRequest) returns (AnalyzeEntitiesResponse)` 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. This method can only analyze documents written in English. Authorization scopes Requires one of the following OAuth scopes: `https://www.googleapis.com/auth/cloud-healthcare` `https://www.googleapis.com/auth/cloud-platform` For more information, see the Authentication Overview.

rpc AnalyzeEntities(AnalyzeEntitiesRequest) returns (AnalyzeEntitiesResponse)

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. This method can only analyze documents written in English.

Authorization scopes

Requires one of the following OAuth scopes:

https://www.googleapis.com/auth/cloud-healthcare
https://www.googleapis.com/auth/cloud-platform

For more information, see the Authentication Overview.

AnalyzeEntitiesRequest

The request to analyze healthcare entities in a document.

Fields
`nlp_service`	`string` The resource name of the service of the form: "projects/{project_id}/locations/{location_id}/services/nlp".
`document_content`	`string` document_content is a document to be annotated.
`licensed_vocabularies[]`	`LicensedVocabulary` A list of licensed vocabularies to use in the request, in addition to the default unlicensed vocabularies.
`alternative_output_format`	`AlternativeOutputFormat` Optional. Alternative output format to be generated based on the results of analysis.

AlternativeOutputFormat

Predefined list of available alternative output formats

Enums
`ALTERNATIVE_OUTPUT_FORMAT_UNSPECIFIED`	No alternative output format is specified.
`FHIR_BUNDLE`	FHIR bundle output.

LicensedVocabulary

Predefined list of available licensed vocabularies

Enums
`LICENSED_VOCABULARY_UNSPECIFIED`	No licensed vocabulary specified.
`ICD10CM`	ICD-10-CM vocabulary
`SNOMEDCT_US`	SNOMED CT (US version) vocabulary

AnalyzeEntitiesResponse

Includes recognized entity mentions and relationships between them.

Fields
`entity_mentions[]`	`EntityMention` The `entity_mentions` field contains all the annotated medical entities that were mentioned in the provided document.
`entities[]`	`Entity` 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.
`relationships[]`	`EntityMentionRelationship` relationships contains all the binary relationships that were identified between entity mentions within the provided document.
Union field `alternative_output_format`. The alternative supported format if the config was included in the request. `alternative_output_format` can be only one of the following:
`fhir_bundle`	`string` The FHIR bundle (`R4`) that includes all the entities, the entity mentions, and the relationships in JSON format.

Entity

The candidate entities that an entity mention could link to.

Fields

Fields
`entity_id`	`string` entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, "UMLS/C0000970".
`preferred_term`	`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.
`vocabulary_codes[]`	`string` 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".

entity_id

string

entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, "UMLS/C0000970".

preferred_term

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.

vocabulary_codes[]

string

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".

EntityMention

An entity mention in the document.

Fields
`mention_id`	`string` mention_id uniquely identifies each entity mention in a single response.
`type`	`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.
`text`	`TextSpan` text is the location of the entity mention in the document.
`linked_entities[]`	`LinkedEntity` linked_entities are candidate ontological concepts that this entity mention may refer to. They are sorted by decreasing confidence.
`temporal_assessment`	`Feature` How this entity mention relates to the subject temporally. Its value is one of: CURRENT, CLINICAL_HISTORY, FAMILY_HISTORY, UPCOMING, ALLERGY
`certainty_assessment`	`Feature` The certainty assessment of the entity mention. Its value is one of: LIKELY, SOMEWHAT_LIKELY, UNCERTAIN, SOMEWHAT_UNLIKELY, UNLIKELY, CONDITIONAL
`subject`	`Feature` The subject this entity mention relates to. Its value is one of: PATIENT, FAMILY_MEMBER, OTHER
`confidence`	`double` The model's confidence in this entity mention annotation. A number between 0 and 1.
`additional_info[]`	`Feature` Additional information about the entity mention. For example, for an entity mention of type `DATE` this can be its more specific date types from the following list: `ADMISSION_DATE`, `CONSULTATION_DATE`, `DISCHARGE_DATE`, `SERVICE_DATE`, `VISIT_DATE`, `DIAGNOSIS_DATE`, `MED_STARTED_DATE`, `MED_ENDED_DATE`, `NOTE_DATE`, `PROCEDURE_DATE`, `RADIATION_STARTED_DATE`, `RADIATION_ENDED_DATE`, `STAGE_DATE`

Feature

A feature of an entity mention.

Fields

Fields
`value`	`string` The value of this feature annotation. Its range depends on the type of the feature.
`confidence`	`double` The model's confidence in this feature annotation. A number between 0 and 1.

value

string

The value of this feature annotation. Its range depends on the type of the feature.

confidence

double

The model's confidence in this feature annotation. A number between 0 and 1.

LinkedEntity

EntityMentions can be linked to multiple entities using a LinkedEntity message lets us add other fields, e.g. confidence.

Fields

Fields
`entity_id`	`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.

entity_id

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.

EntityMentionRelationship

Defines directed relationship from one entity mention to another.

Fields

Fields
`subject_id`	`string` subject_id is the id of the subject entity mention.
`object_id`	`string` object_id is the id of the object entity mention.
`confidence`	`double` The model's confidence in this annotation. A number between 0 and 1.

subject_id

string

subject_id is the id of the subject entity mention.

object_id

string

object_id is the id of the object entity mention.

confidence

double

The model's confidence in this annotation. A number between 0 and 1.

TextSpan

A span of text in the provided document.

Fields

Fields
`content`	`string` The original text contained in this span.
`begin_offset`	`int32` The unicode codepoint index of the beginning of this span.

content

string

The original text contained in this span.

begin_offset

int32

The unicode codepoint index of the beginning of this span.