Description
This pretrained pipeline is built on the top of re_temporal_events_enriched_clinical 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
pipeline = PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
val pipeline = new PretrainedPipeline("re_temporal_events_enriched_clinical_pipeline", "en", "clinical/models")
pipeline.annotate("The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.")
import nlu
nlu.load("en.relation.temproal_enriched.pipeline").predict("""The patient is a 56-year-old right-handed female with longstanding intermittent right low back pain, who was involved in a motor vehicle accident in September of 2005. At that time, she did not notice any specific injury, but five days later, she started getting abnormal right low back pain.""")
Results
+----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
| | relation | entity1 | entity1_begin | entity1_end | chunk1 | entity2 | entity2_begin | entity2_end | chunk2 | confidence |
+====+============+===========+=================+===============+===============================================+============+=================+===============+==========================+==============+
| 0 | OVERLAP | PROBLEM | 54 | 98 | longstanding intermittent right low back pain | OCCURRENCE | 121 | 144 | a motor vehicle accident | 0.532308 |
+----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
| 1 | AFTER | DATE | 171 | 179 | that time | PROBLEM | 201 | 219 | any specific injury | 0.577288 |
+----+------------+-----------+-----------------+---------------+-----------------------------------------------+------------+-----------------+---------------+--------------------------+--------------+
Model Information
| Model Name: | re_temporal_events_enriched_clinical_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