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.
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 |
---|---|
|
Document Section |
|
Anatomy |
|
Medical Condition |
|
Medicine |
|
Physical Examination |
|
Laboratory Examination |
|
Treatment and Procedures |
|
Measurements |
|
General |
|
Allergy |
|
Immunization |
|
ROLE |
|
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 |