chore: update docs/dyn (#1106)
diff --git a/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html b/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
index 1e3cb8e..e1bea7c 100644
--- a/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
+++ b/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
@@ -103,13 +103,11 @@
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.
- "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",
- ],
- "entityId": "A String", # entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, "UMLS/C0000970".
+ "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.
+ "subjectId": "A String", # subject_id is the id of the subject entity mention.
+ "objectId": "A String", # object_id is the id of the object entity mention.
},
],
"entityMentions": [ # entity_mentions contains all the annotated medical entities that were were mentioned in the provided document.
@@ -119,32 +117,34 @@
"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.
},
],
+ "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.
"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
+ "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 feature annotation. A number between 0 and 1.
+ },
+ "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
+ "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 feature annotation. A number between 0 and 1.
+ },
+ "confidence": 3.14, # The model's confidence in this entity mention annotation. A number between 0 and 1.
+ "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
+ "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 feature annotation. A number between 0 and 1.
+ },
"text": { # A span of text in the provided document. # text is the location of the entity mention in the document.
"content": "A String", # The original text contained in this span.
"beginOffset": 42, # The unicode codepoint index of the beginning of this span.
},
- "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.
- },
- "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.
- },
- "confidence": 3.14, # The model's confidence in this entity mention annotation. A number between 0 and 1.
- "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.
- "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.
- },
},
],
- "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.
+ "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",
+ ],
},
],
}</pre>