Healthcare NLP Models

Learn about the Language service Healthcare NLP models to extract entities from healthcare records such as electronic health records (EHR), progress notes, and clinical trial documents.

The Healthcare NLP model is used to process healthcare text records such as EHR to extract entities, determine assertion statuses, identify related entities, and link those entities with SNOMED, ICD10 ontologies.

Healthcare NLP is a suite of four models:

Health Named Entity Recognition
Identifies key entities from text. This includes identifying medical conditions, medications, dosages, symptoms, test results, treatments, and procedures.
Relationship Extraction
Identify relationships between different medical entities. For example, it extracts the relationship between the medication and dosage.
Assertion Detection
Identifies contextual modifiers to the extracted entities such as the status, subject, time, and so on.
Medical Entity Linking
Link the extracted entities with codes from the biomedical vocabularies, SNOMED-CT and ICD-10.
Note

When working with the Oracle NLP model, it's important to review the provided confidence scores for accuracy. These scores can help you determine the appropriate confidence threshold for your particular use case. However, to ensure compliance with regulations, it's always advisable to verify the accuracy of any detected Health entities through other means such as human review.

Sample Use Cases

Healthcare NLP models have a wide range of use cases in healthcare, revolutionizing the industry by improving patient care, streamlining operations, and facilitating research.

Clinical Documentation Improvement
NLP models can analyze clinical notes and suggest improvements, ensuring accurate and complete patient records for billing and compliance purposes.
Clinical Decision Support
NLP can help providers by extracting relevant information from patient records to provide recommendations for treatment options.
Medical Coding
NLP can help automate the coding of medical procedures and diagnoses by analyzing physician notes.
Tele Medicine
Develop voice-activated assistants that can transcribe doctor-patient interactions, update electronic health records, and provide quick access to relevant patient data during appointments.

Supported Language

The PHI model supports English (United States).

Supported Entity Types

Entity Type Description

HEADER

SUB_HEADER

Document Section

ANATOMICAL_SITE

Anatomy

SIGN_SYMPTOM

DIAGNOSIS

OBSERVATION

MODIFIER

Medical Condition

MEDICINE_NAME

MEDICINE_FREQUENCY

MEDICINE_DOSE

MEDICINE_DOSE.FORM

MEDICINE_ROUTE

MEDICINE_DURATION

MEDICINE_STRENGTH

MEDICATION_CLASS

MEDICINE_REFILL_AMOUNT

Medicine

VITALS

OBSERVABLE_ENTITY

Physical Examination

LAB_TEST

Laboratory Examination

TREATMENT

PROCEDURE

Treatment and Procedures

MEASUREMENT

Measurements

DIRECTION

RESULT

General

ALLERGEN_AGENT

Allergy

IMMUNIZATION

Immunization

ROLE

ROLE

FAMILY_RELATION

FAMILY Relation

Supported Ontologies

  • SNOMED CT US
  • ICD-10-CM

Supported Assertion Types

Assertion Type Possible Values

Status

Negated

Certainty

Certain

Possible

Hypothetical

Conditional

Uncertain

Temporality

Past

Present

Future

Subject

Physician

Patient

Family

Other

Action

Start

Stop

Increase

Decrease

OtherChange

Severity

Mild

Moderate

Severe

Chronicity

Acute

Chronic

Acute-on-Chronic

Subacute

Major

Course

Worsening

Improving

Controlled

Uncontrolled

Other

Supported Relation Types

Relation Type Subject Entity Object Entity
ANATOMICAL_SITE_OF_DIAGNOSIS DIAGNOSIS ANATOMICAL_SITE
ANATOMICAL_SITE_OF_OBSERVATIONS OBSERVATION ANATOMICAL_SITE
ANATOMICAL_SITE_OF_SIGN_SYMPTOM SIGN_SYMPTOM ANATOMICAL_SITE
ANATOMICAL_SITE_OF_PROCEDURE PROCEDURE ANATOMICAL_SITE
DOSAGE_OF_MEDICINE MEDICINE_NAME MEDICINE_DOSAGE
DURATION_OF_MEDICINE MEDICINE_NAME MEDICINE_DURATION
ROUTE_MODE_OF_MEDICINE MEDICINE_NAME MEDICINE_ROUTE_MODE
STRENGTH_OF_MEDICINE MEDICINE_NAME MEDICINE_STRENGTH
FREQUENCY_OF_MEDICINE MEDICINE_NAME MEDICINE_FREQUENCY
RESULT_OF_LAB_TEST LAB_TEST RESULT
MEASUREMENT_OF_LAB_TEST PHYSICAL_EXAMINATION MEASUREMENT
MEASUREMENT_OF_PHYSICAL_EXAMINATION PHYSICAL_EXAMINATION MEASUREMENT
RESULT_OF_VITALS PHYSICAL_EXAMINATION RESULT
RESULT_OF_OBSERVABLE_ENTITY OBSERVABLE_ENTITY RESULT
DIRECTION_OF_ANATOMICAL_SITE ANATOMICAL_SITE DIRECTION
ANATOMICAL_SITE_OF_TREATMENT TREATMENT ANATOMICAL_SITE
MODIFIER_OF_DIAGNOSIS DIAGNOSIS MODIFIER
MODIFIER_OF_PHYSICAL-EXAMINATION PHYSICAL_EXAMINATION MODIFIER
MODIFIER_OF_SIGN_SYMPTOM SIGN_SYMPTOM MODIFIER
MODIFIER_OF_OBSERVATION OBSERVATION MODIFIER
MODIFIER_OF_MEDICINE_NAME MEDICINE_NAME MODIFIER
MODIFIER_OF_MEDICINE_DOSAGE MEDICINE_DOSAGE MODIFIER
MODIFIER_OF_ANATOMICAL_SITE ANATOMICAL_SITE MODIFIER
TIME_OF_PROCEDURE PROCEDURE DATETIME
TIME_OF_SIGN_SYMPTOM SIGN_SYMPTOM DATETIME
TIME_OF_DIAGNOSIS DIAGNOSIS DATETIME
TIME_OF_LAB_TEST LAB_TEST DATETIME