Description
This pretrained pipeline is built on the top of re_bodypart_proceduretest 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
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("re_bodypart_proceduretest_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.")
import nlu
nlu.load("en.relation.bodypart_proceduretest.pipeline").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
Results
| index | relations | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2 | confidence |
|-------|-----------|------------------------------|---------------|-------------|--------|---------|-------------|-------------|---------------------|------------|
| 0 | 1 | External_body_part_or_region | 94 | 98 | chest | Test | 117 | 135 | portable ultrasound | 1.0 |
Model Information
| Model Name: | re_bodypart_proceduretest_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- PerceptronModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- DependencyParserModel
- RelationExtractionModel