Description
This pretrained pipeline is built on the top of re_bodypart_directions model.
Predicted Entities
Admission_Discharge, Age, Alcohol, Allergen, BMI, Birth_Entity, Blood_Pressure, Cerebrovascular_Disease, Clinical_Dept, Communicable_Disease, Date, Death_Entity, Diabetes, Diet, Direction, Disease_Syndrome_Disorder, Dosage, Drug, Duration, EKG_Findings, Employment, External_body_part_or_region, Family_History_Header, Fetus_NewBorn, Form, Frequency, Gender, HDL, Heart_Disease, Height, Hyperlipidemia, Hypertension, ImagingFindings, Imaging_Technique, Injury_or_Poisoning, Internal_organ_or_component, Kidney_Disease, LDL, Labour_Delivery, Medical_Device, Medical_History_Header, Modifier, O2_Saturation, Obesity, Oncological, Overweight, Oxygen_Therapy, Pregnancy, Procedure, Psychological_Condition, Pulse, Race_Ethnicity, Relationship_Status, RelativeDate, RelativeTime, Respiration, Route, Section_Header, Sexually_Active_or_Sexual_Orientation, Smoking, Social_History_Header, Strength, Substance, Substance_Quantity, Symptom, Temperature, Test, Test_Result, Time, Total_Cholesterol, Treatment, Triglycerides, VS_Finding, Vaccine, Vital_Signs_Header, Weight
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("re_bodypart_directions_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia")
import nlu
nlu.load("en.relation.bodypart_directions.pipeline").predict("""MRI demonstrated infarction in the upper brain stem , left cerebellum and right basil ganglia""")
Results
| index | relations | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_end | entity2_end | chunk2 | confidence |
|-------|-----------|-----------------------------|---------------|-------------|------------|-----------------------------|-------------|-------------|---------------|------------|
| 0 | 1 | Direction | 35 | 39 | upper | Internal_organ_or_component | 41 | 50 | brain stem | 0.9999989 |
| 1 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 59 | 68 | cerebellum | 0.99992585 |
| 2 | 0 | Direction | 35 | 39 | upper | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.9999999 |
| 3 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 54 | 57 | left | 0.999811 |
| 4 | 0 | Internal_organ_or_component | 41 | 50 | brain stem | Direction | 75 | 79 | right | 0.9998203 |
| 5 | 1 | Direction | 54 | 57 | left | Internal_organ_or_component | 59 | 68 | cerebellum | 1.0 |
| 6 | 0 | Direction | 54 | 57 | left | Internal_organ_or_component | 81 | 93 | basil ganglia | 0.97616416 |
| 7 | 0 | Internal_organ_or_component | 59 | 68 | cerebellum | Direction | 75 | 79 | right | 0.953046 |
| 8 | 1 | Direction | 75 | 79 | right | Internal_organ_or_component | 81 | 93 | basil ganglia | 1.0 |
Model Information
| Model Name: | re_bodypart_directions_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