1<html><body> 2<style> 3 4body, h1, h2, h3, div, span, p, pre, a { 5 margin: 0; 6 padding: 0; 7 border: 0; 8 font-weight: inherit; 9 font-style: inherit; 10 font-size: 100%; 11 font-family: inherit; 12 vertical-align: baseline; 13} 14 15body { 16 font-size: 13px; 17 padding: 1em; 18} 19 20h1 { 21 font-size: 26px; 22 margin-bottom: 1em; 23} 24 25h2 { 26 font-size: 24px; 27 margin-bottom: 1em; 28} 29 30h3 { 31 font-size: 20px; 32 margin-bottom: 1em; 33 margin-top: 1em; 34} 35 36pre, code { 37 line-height: 1.5; 38 font-family: Monaco, 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', 'Lucida Console', monospace; 39} 40 41pre { 42 margin-top: 0.5em; 43} 44 45h1, h2, h3, p { 46 font-family: Arial, sans serif; 47} 48 49h1, h2, h3 { 50 border-bottom: solid #CCC 1px; 51} 52 53.toc_element { 54 margin-top: 0.5em; 55} 56 57.firstline { 58 margin-left: 2 em; 59} 60 61.method { 62 margin-top: 1em; 63 border: solid 1px #CCC; 64 padding: 1em; 65 background: #EEE; 66} 67 68.details { 69 font-weight: bold; 70 font-size: 14px; 71} 72 73</style> 74 75<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> 76<h2>Instance Methods</h2> 77<p class="toc_element"> 78 <code><a href="#analyzeEntities">analyzeEntities(nlpService, body=None, x__xgafv=None)</a></code></p> 79<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> 80<p class="toc_element"> 81 <code><a href="#close">close()</a></code></p> 82<p class="firstline">Close httplib2 connections.</p> 83<h3>Method Details</h3> 84<div class="method"> 85 <code class="details" id="analyzeEntities">analyzeEntities(nlpService, body=None, x__xgafv=None)</code> 86 <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. 87 88Args: 89 nlpService: string, The resource name of the service of the form: "projects/{project_id}/locations/{location_id}/services/nlp". (required) 90 body: object, The request body. 91 The object takes the form of: 92 93{ # The request to analyze healthcare entities in a document. 94 "documentContent": "A String", # document_content is a document to be annotated. 95 "licensedVocabularies": [ # A list of licensed vocabularies to use in the request, in addition to the default unlicensed vocabularies. 96 "A String", 97 ], 98} 99 100 x__xgafv: string, V1 error format. 101 Allowed values 102 1 - v1 error format 103 2 - v2 error format 104 105Returns: 106 An object of the form: 107 108 { # Includes recognized entity mentions and relationships between them. 109 "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. 110 { # The candidate entities that an entity mention could link to. 111 "entityId": "A String", # entity_id is a first class field entity_id uniquely identifies this concept and its meta-vocabulary. For example, "UMLS/C0000970". 112 "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. 113 "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". 114 "A String", 115 ], 116 }, 117 ], 118 "entityMentions": [ # entity_mentions contains all the annotated medical entities that were mentioned in the provided document. 119 { # An entity mention in the document. 120 "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 121 "confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1. 122 "value": "A String", # The value of this feature annotation. Its range depends on the type of the feature. 123 }, 124 "confidence": 3.14, # The model's confidence in this entity mention annotation. A number between 0 and 1. 125 "linkedEntities": [ # linked_entities are candidate ontological concepts that this entity mention may refer to. They are sorted by decreasing confidence.it 126 { # EntityMentions can be linked to multiple entities using a LinkedEntity message lets us add other fields, e.g. confidence. 127 "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. 128 }, 129 ], 130 "mentionId": "A String", # mention_id uniquely identifies each entity mention in a single response. 131 "subject": { # A feature of an entity mention. # The subject this entity mention relates to. Its value is one of: PATIENT, FAMILY_MEMBER, OTHER 132 "confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1. 133 "value": "A String", # The value of this feature annotation. Its range depends on the type of the feature. 134 }, 135 "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 136 "confidence": 3.14, # The model's confidence in this feature annotation. A number between 0 and 1. 137 "value": "A String", # The value of this feature annotation. Its range depends on the type of the feature. 138 }, 139 "text": { # A span of text in the provided document. # text is the location of the entity mention in the document. 140 "beginOffset": 42, # The unicode codepoint index of the beginning of this span. 141 "content": "A String", # The original text contained in this span. 142 }, 143 "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. 144 }, 145 ], 146 "relationships": [ # relationships contains all the binary relationships that were identified between entity mentions within the provided document. 147 { # Defines directed relationship from one entity mention to another. 148 "confidence": 3.14, # The model's confidence in this annotation. A number between 0 and 1. 149 "objectId": "A String", # object_id is the id of the object entity mention. 150 "subjectId": "A String", # subject_id is the id of the subject entity mention. 151 }, 152 ], 153}</pre> 154</div> 155 156<div class="method"> 157 <code class="details" id="close">close()</code> 158 <pre>Close httplib2 connections.</pre> 159</div> 160 161</body></html>