Description
This pretrained pipeline is built on the top of re_test_result_date model.
Predicted Entities
Injury_or_Poisoning
, Direction
, Test
, Route
, Admission_Discharge
, Death_Entity
, Triglycerides
, Oxygen_Therapy
, Relationship_Status
, Duration
, Alcohol
, Date
, Drug
, Hyperlipidemia
, Respiration
, Birth_Entity
, VS_Finding
, Age
, Social_History_Header
, Family_History_Header
, Medical_Device
, Labour_Delivery
, BMI
, Fetus_NewBorn
, Temperature
, Section_Header
, Communicable_Disease
, ImagingFindings
, Psychological_Condition
, Obesity
, Sexually_Active_or_Sexual_Orientation
, Modifier
, Vaccine
, Symptom
, Pulse
, Kidney_Disease
, Oncological
, EKG_Findings
, Medical_History_Header
, Cerebrovascular_Disease
, Blood_Pressure
, Diabetes
, O2_Saturation
, Heart_Disease
, Employment
, Frequency
, Disease_Syndrome_Disorder
, Pregnancy
, RelativeDate
, Procedure
, Overweight
, Race_Ethnicity
, Hypertension
, External_body_part_or_region
, Imaging_Technique
, Test_Result
, Treatment
, Substance
, Clinical_Dept
, LDL
, Diet
, Substance_Quantity
, Allergen
, Gender
, RelativeTime
, Total_Cholesterol
, Internal_organ_or_component
, Smoking
, Vital_Signs_Header
, Height
, Form
, Strength
, Weight
, Time
, Dosage
, HDL
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("re_test_result_date_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%")
import nlu
nlu.load("en.relation.date_test_result.pipeline").predict("""He was advised chest X-ray or CT scan after checking his SpO2 which was <= 93%""")
Results
| index | relations | entity1 | chunk1 | entity2 | chunk2 |
|-------|--------------|--------------|---------------------|--------------|---------|
| 0 | O | TEST | chest X-ray | MEASUREMENTS | 93% |
| 1 | O | TEST | CT scan | MEASUREMENTS | 93% |
| 2 | is_result_of | TEST | SpO2 | MEASUREMENTS | 93% |
Model Information
Model Name: | re_test_result_date_pipeline |
Type: | pipeline |
Compatibility: | Healthcare NLP 4.4.4+ |
License: | Licensed |
Edition: | Official |
Language: | en |
Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- PerceptronModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- DependencyParserModel
- RelationExtractionModel