Healthcare Natural Language API

The Healthcare Natural Language API is a part of the Cloud Healthcare API that uses natural language models to extract healthcare information from medical text.

This conceptual guide explains the basics of using the Healthcare Natural Language API, including:

  • The types of requests you can make to the Healthcare Natural Language API
  • How to construct requests to the Healthcare Natural Language API
  • How to handle responses from the Healthcare Natural Language API


The Healthcare Natural Language API extracts healthcare information from medical text. This healthcare information can include:

  • Medical concepts, such as medications, procedures, and medical conditions
  • Functional features, such as temporal relationships, subjects, and certainty assessments
  • Relations, such as side effects and medication dosage

Choosing between the Healthcare Natural Language API and AutoML Entity Extraction for Healthcare

The Healthcare Natural Language API offers pre-trained natural language models to extract medical concepts and relationships from medical text. The Healthcare Natural Language API maps text into a predefined set of medical knowledge categories.

AutoML Entity Extraction for Healthcare lets you create a custom entity extraction model trained using your own annotated medical text and using your own categories. For more information, see the AutoML Entity Extraction for Healthcare documentation.

Available locations

The Healthcare Natural Language API is available in the following locations:

Location name Location description
asia-south1 Mumbai, India
australia-southeast1 Sydney, Australia
europe-west2 London, UK
europe-west4 Netherlands
northamerica-northeast1 Montréal, Canada
us-central1 Iowa, USA

Healthcare Natural Language API features

The Healthcare Natural Language API inspects medical text for medical concepts and relations. You perform entity analysis using the analyzeEntities method.

Entity analysis requests

The Healthcare Natural Language API is a REST API and consists of JSON requests and responses. The following sections show how to extract different medical insights from a given medical text:

The entity analysis request contains the following fields:

Entity analysis response fields

The entity analysis returns a set of detected medical knowledge mentions, medical concepts, and relations between medical knowledge mentions, including the following:

  • entityMentions: occurrences of medical knowledge entities in the source medical text. Each entity mention has the following fields:

    • mentionId: a unique identifier for an entity mention in the response.
    • type: the medical knowledge category of the entity mention.
    • text: consists of the textContent field, and describes the excerpt of the medical text containing the entity mention, and offset, the location of the entity mention in the source medical text.
    • temporalAssessment: specifies how the linked entity relates to the entity mention, one of CURRENT, CLINICAL_HISTORY, FAMILY_HISTORY, UPCOMING, or OTHER.
    • certaintyAssessment: the negation or qualification of the medical concept, one of LIKELY, SOMEWHAT_LIKELY, UNCERTAIN, SOMEWHAT_UNLIKELY, UNLIKELY, or CONDITIONAL.
    • subject: specifies the subject that the medical concept relates to, one of PATIENT, FAMILY_MEMBER, or OTHER.
    • linkedEntities: a list of medical concepts that might be related to this entity mention. Linked entities specify the entityId, which links a medical concept to an an entity in entities.
  • entities: describes the medical concepts from the linked entities fields. Each entity is described using the following fields:

    • entityId: a unique identifier from the linkedEntities field.
    • preferredTerm: a preferred term for the medical concept.
    • vocabularyCodes: the representation of the medical concept in supported medical vocabularies.
  • relationships: define directed relationships between entity mentions. In the sample, the subject of the relationship is "Insulin regimen human" and the object of the relationship is "5 units".

  • confidence: an indication of the model's confidence in the relationship as a number between 0 and 1.

Apart from the listed fields, the response might also contain the additionalInfo field, which states any additional description about the entity mention type. See Additional information.

Supported languages

The Healthcare Natural Language API only supports extracting healthcare information from English text.

Supported medical vocabularies

The Healthcare Natural Language API supports the following medical vocabularies:

  • Foundational Model of Anatomy
  • Gene Ontology
  • HUGO Gene Nomenclature Committee
  • Human Phenotype Ontology
  • ICD-10 Procedure Coding System
  • ICD-10-CM
  • ICD-9-CM
  • MeSH
  • MedlinePlus Health Topics
  • Metathesaurus Names
  • NCBI Taxonomy
  • NCI Thesaurus
  • National Drug File
  • Online Mendelian Inheritance in Man
  • SNOMED CT (available for US users only)

Supported medical knowledge categories

The Healthcare Natural Language API assigns a medical knowledge category to the entityMentions.type field. A list of supported medical knowledge categories is as follows. The entity mention types that belong to the oncology, social determinants of health (SDOH), and protected health information (PHI) groups are only available in Preview:

Group Medical knowledge category Description
General ANATOMICAL_STRUCTURE A complex part of the human body, such as cells, organs, and systems.
BODY_FUNCTION A function carried out by the human body.
BF_RESULT The result of a body function.
BODY_MEASUREMENT A normal measurement of the human body, such as vital signs, obtained without any complex tests or procedures using basic instruments, such as a thermometer or a stethoscope.
BM_RESULT The result of a body measurement.
BM_UNIT The unit for a body measurement.
BM_VALUE The value of a body measurement.
LABORATORY_DATA The results of testing a bodily sample.
LAB_RESULT A qualitative description of laboratory data, such as "increased", "decreased", "positive", or "negative".
LAB_VALUE The value of an instance of the laboratory data.
LAB_UNIT The unit of measurement for the laboratory value.
MEDICAL_DEVICE A physical or virtual instrument.
MEDICINE A drug or other preparation for the treatment or prevention of a disease.
MED_DOSE A dose of a medication.
MED_DURATION The period of administration of a medication.
MED_FORM The physical characteristics of a specific medication.
MED_FREQUENCY The frequency at which a medication is taken.
MED_ROUTE A location in the body where a medication is administered.
MED_STATUS The status of an existing medication, such as "continue", "start", "restart", "stop", "switch", "increase", and "decrease".
MED_STRENGTH The amount of active ingredient in a dose of a medication.
MED_UNIT The unit of measurement for the active ingredient in a medication.
MED_TOTALDOSE The quantity of medication to take at one time.
PROBLEM A medical condition, including findings and diseases.
PROCEDURE A diagnostic or treatment procedure.
PROCEDURE_RESULT The results of a procedure.
PROC_METHOD The method used to conduct a procedure.
SEVERITY The severity of the medical condition.
SUBSTANCE_ABUSE A description of abuse of a psychoactive substance.
Oncology (Preview) CLINICAL_STATUS The status of a cancer case such as "active", "recurring", "relapsing", and "resolved".
DATE A date annotation, such as the date of diagnosis, date of procedure, or date of a radiation treatment. It extracts all the elements of a date and might not include the year.
DIMENSIONS The measurements of a tumor, a mass, or an abnormal growth.
GENE_STUDIED The genes studied which directly or indirectly lead to tumor formation, such as BRCA1, p53, and ALK.
HISTOLOGICAL_GRADE A classification system to grade the appearance of a cancerous cell.
LAB_SPECIMEN Biological material collected from the body for testing or sampling.
RADIATION_DOSAGE The amount of radiation given to a patient.
ONSET A date annotation to represent the date when a patient first observed cancer-related problems.
VARIATION_CODE A code given to the specific genomic variant that's detected under a major coding system such as ClinVar and HGVS.
Social determinants of health (SDoH) (Preview) AGE An age identifier. It includes phrases describing age, such as "looks younger than stated age", "middle-aged", "78 years old", or "teen".

Note: HIPAA classifies the age of a person as PHI only when it's above 90. For more information, see Summary of the HIPAA Privacy Rule.

FAMILY Phrases describing the patient's family structure or relatives, such as "married with two kids", "brother", "wife", "supportive parents", or "divorced".
LIVING_SITUATION Phrases describing the patient's living situation, such as "with roommates", "has 24/7 homecare nurse", or "moved recently".
SOCIAL_IDENTITY Phrases describing the patient's or the family's social identity including race, ethnicity, sexual orientation, religion, nationality, languages spoken or not spoken, or country of origin.
PHYSICAL_APPEARANCE Phrases describing the patient's or the family's most noticeable or evident physical trait, such as "scar on the right cheek", "Down's syndrome", "obese", or "left leg amputated".
OCCUPATION Phrases describing the patient's or family's occupation and employment status, such as "retired mother", "worked as a welder for 20 years", or "lost job last year".
Protected health information (PHI) (Preview) PERSON_NAME A generic name identifier for a person. Includes titles, such as "Dr.", "Mrs.", or "MD"
ORGANIZATION_NAME An identifier for a medical organization that collects PHI, such as a clinic, nursing home, or hospital.
GENERIC_ID A generic ID that identifies medical records, patients, doctors, hospitals, such as the patient's SSN, or a provider's number.
LOCATION A geographic location that might contain names and numbers for buildings, streets, cities, states, or ZIP code.
PHONE_NUMBER A number to indicate a phone number, fax number, or pager number.
EMAIL_ADDRESS An email address identifier.
URL A website's address.
ZIPCODE A ZIP code identifier.

Supported functional feature categories

The Healthcare Natural Language API can infer functional features, or attributes, of an entity mention from context. For example, in the statement "Kusuma's mother has diabetes", the condition "diabetes" has the functional feature of subject FAMILY_MEMBER.

Temporal relationships

Temporal relationships, returned in the temporalAssessment field, describe how this entity mention relates to the subject temporally.

The Healthcare Natural Language API supports the following temporal relationships:



Subjects, returned in the subject field, describe the individual the entity mention relates to.

The Healthcare Natural Language API supports the following subjects:


Certainty assessments

Certainty assessments, returned in the certaintyAssessment field, describe the original note taker's confidence. For example, if the original note contains "The patient has a sore throat", the certainty assessment returns a LIKELY value to indicate the note taker's confidence that it was likely that the patient had a sore throat. If the original note contains "The patient does not have a sore throat", the certainty assessment returns an UNLIKELY value to indicate the note taker's confidence that it was unlikely that the patient had a sore throat.

Certainty assessments can be one of the following values:


Additional information

The additionalInfo field provides additional details about an entity mention. For example, the additionalInfo field for a DATE entity mention might consist of details about the type of the date, categorized as one of the following:


Supported relationships between entity mentions

The Healthcare Natural Language API can infer relationships between entity mentions based on the surrounding medical text. In the response, the subject of the relationship is identified by subjectId and the object of the relationship is identified by objectId.

The Healthcare Natural Language API supports the following relationships between entity mentions:

Subject Object

Healthcare Natural Language API output as a FHIR bundle

When you request the analyzeEntities method with the alternativeOutputFormat field set to FHIR_BUNDLE, the response includes the following JSON objects:

  • The entity mentions, the entities, and the relationships
  • A FHIR R4 bundle represented as a string, that includes all the entities, the entity mentions, and the relationships in JSON format

To create the FHIR R4 bundle, the Healthcare Natural Language API maps the entity mentions, entities, and relationships to FHIR resources and their elements. The following table lists some of these mappings.

Healthcare Natural Language API entity mentions Medical Knowledge Category FHIR R4 resources and elements
PROBLEM Condition
PROBLEM Condition.category
PROBLEM Condition.status
PROBLEM SEVERITY Condition.severity
PROCEDURE Procedure.status
PROCEDURE Procedure.code
PROCEDURE PROBLEM Procedure.reasonReference
MEDICINE MedicationStatement
MEDICINE MedicationStatement.status
MEDICINE MedicationStatement.medication
MEDICINE PROBLEM MedicationStatement.reasonReference
MEDICINE MED_DOSE MedicationStatement.dosage.doseAndRate.doseQuantity
MEDICINE MED_FREQUENCY MedicationStatement.dosage.text
MEDICINE MED_ROUTE MedicationStatement.dosage.route
MEDICINE MED_STRENGTH MedicationStatement.dosage.doseAndRate.doseQuantity
MEDICINE MED_UNIT MedicationStatement.dosage.doseAndRate.doseQuantity

To extract entities from text as a FHIR R4 bundle, see Extract output as a FHIR R4 bundle.