Pipeline to Detect Temporal Relations for Clinical Events (Enriched)

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

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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