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
| | sentence | entity1_begin | entity1_end | chunk1 | entity1 | entity2_begin | entity2_end | chunk2 | entity2 | relation | confidence |
|--:|---------:|--------------:|------------:|------------:|--------:|--------------:|------------:|------------:|------------:|--------------:|-----------:|
| 0 | 0 | 0 | 1 | He | Gender | 15 | 25 | chest X-ray | Test | is_finding_of | 0.9991597 |
| 1 | 0 | 0 | 1 | He | Gender | 30 | 36 | CT scan | Test | is_finding_of | 1.0 |
| 2 | 0 | 15 | 25 | chest X-ray | Test | 30 | 36 | CT scan | Test | is_finding_of | 1.0 |
| 3 | 0 | 30 | 36 | CT scan | Test | 53 | 55 | his | Gender | is_finding_of | 1.0 |
| 4 | 0 | 30 | 36 | CT scan | Test | 57 | 60 | SpO2 | Test | is_finding_of | 1.0 |
| 5 | 0 | 53 | 55 | his | Gender | 57 | 60 | SpO2 | Test | is_date_of | 0.98956 |
| 6 | 0 | 53 | 55 | his | Gender | 75 | 77 | 93% | Test_Result | is_date_of | 0.9999974 |
| 7 | 0 | 57 | 60 | SpO2 | Test | 75 | 77 | 93% | Test_Result | is_result_of | 0.92868817 |
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