Extract entities from patient narratives

This model has been trained to automatically identify and extract named entities from text data related to patient health and medical issues. The model has been specifically trained on text data that has been scraped from medical forums, where patients may discuss their health conditions, symptoms, and experiences with medical treatments.

Use the provided Streamlit playground application to test this service.

**Entity Recognition**: Initially, the model accurately identifies entities such as InjuryOrPoisoning, Substance, Form, Frequency, Employment, Drug, Route, Disease, Gender, Dosage, Employment, Procedure, RelationshipStatus, ClinicalDept, Symptom, VitalTest, Laterality, PsychologicalCondition,Modifier, Age, Vaccine, TestResult, HealthStatus, AdmissionDischarge, Allergen, DateTime, MedicalDevice, SubstanceQuantity, Treatment, BodyPart, Test, Duration

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**Assertion Status Detection**: Subsequently, it assigns an assertion status to each identified entity (e.g. Present_Or_Past, Hypothetical_Or_Absent, SomeoneElse)

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**Relation Extraction Labels:** The final step involves the detection of relationships between the extracted entities, identifying relation between specific dates with specific symptom, disease, treatment, test, drug and more or relations between drugs and diseases , disease and procedures, disease and allergens, (e.g. DateTime-Symptom, DateTime-Disease, DateTime-PsychologicalCondition, DateTime-InjuryOrPoisoning, DateTime-Drug, DateTime-Substance, DateTime-Procedure, DateTime-Treatment, DateTime-Test, DateTime-TestResult, DateTime-Vaccine, DateTime-AdmissionDischarge, TestResult-Test, TestResult-VitalTest, PsychologicalCondition-Drug, PsychologicalCondition-Procedure, PsychologicalCondition-Treatment, Disease-Drug, Disease-Procedure, Disease-Treatment, Disease-Allergen, Disease-Vaccine, Treatment-Drug, Treatment-Procedure)