chore: update docs/dyn , add static discovery files to discovery_cache/documents (#1111)
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Source-Link: https://github.com/googleapis/synthtool/commit/c2de32114ec484aa708d32012d1fa8d75232daf5
diff --git a/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html b/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
index e1bea7c..4c1127c 100644
--- a/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
+++ b/docs/dyn/healthcare_v1beta1.projects.locations.services.nlp.html
@@ -105,37 +105,37 @@
{ # Includes recognized entity mentions and relationships between them.
"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.
+ "subjectId": "A String", # subject_id is the id of the subject entity mention.
+ "confidence": 3.14, # The model's confidence in this annotation. A number between 0 and 1.
},
],
"entityMentions": [ # entity_mentions contains all the annotated medical entities that were were mentioned in the provided document.
{ # An entity mention in the document.
- "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.
- },
- ],
- "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.
+ "confidence": 3.14, # The model's confidence in this entity mention annotation. A number between 0 and 1.
"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.
},
+ "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.
+ },
"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.
- },
+ "mentionId": "A String", # mention_id uniquely identifies each entity mention in a single response.
"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.
},
+ "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.
+ },
+ ],
+ "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.
},
],
"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.