Description
This pipeline is designed to:
- extract clinical entities
- assign assertion status to the extracted entities
- establish relations between the extracted entities
from clinical texts. In this pipeline, ner_jsl NER model, assertion_jsl assertion model, re_test_result_date, and posology_re relation extraction models were used to achieve those tasks. Here are the NER, assertion, and relation extraction labels this pipeline can extract. Here are the NER, assertion, and relation extraction labels this pipeline can extract.
-
Clinical Entity Labels:
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_BrandName
Drug_Ingredient
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
Vaccine_Name
Vital_Signs_Header
Weight
-
Assertion Status Labels:
Hypothetical
,Someoneelse
,Past
,Absent
,Family
,Planned
,Possible
,Present
-
Relation Extraction Labels:
is_finding_of
,is_date_of
,is_result_of
,Drug_BrandName-Dosage
,Drug_BrandName-Frequency
,Drug_BrandName-Route
,Drug_BrandName-Strength
,Drug_Ingredient-Dosage
,Drug_Ingredient-Frequency
,Drug_Ingredient-Route
,Drug_Ingredient-Strength
,O
Available as Private API Endpoint
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("explain_clinical_doc_granular", "en", "clinical/models")
result = ner_pipeline.annotate("""The patient admitted for gastrointestinal pathology, under working treatment.
History of prior heart murmur with echocardiogram findings as above on March 1998.
According to the latest echocardiogram, basically revealed normal left ventricular function with left atrial enlargement .
Based on the above findings, we will treat her medically with ACE inhibitors 10 mg, p.o, daily. Also we will give Furosemide 40 mg, p.o later and see how she fares.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("explain_clinical_doc_granular", "en", "clinical/models")
val result = ner_pipeline.annotate("""The patient admitted for gastrointestinal pathology, under working treatment.
History of prior heart murmur with echocardiogram findings as above on March 1998.
According to the latest echocardiogram, basically revealed normal left ventricular function with left atrial enlargement .
Based on the above findings, we will treat her medically with ACE inhibitors 10 mg, p.o, daily. Also we will give Furosemide 40 mg, p.o later and see how she fares.""")
Results
# ner
+-----------+-----+---+--------------------------+-------------------+
|sentence_id|begin|end|entity |label |
+-----------+-----+---+--------------------------+-------------------+
|0 |12 |19 |admitted |Admission_Discharge|
|0 |25 |50 |gastrointestinal pathology|Clinical_Dept |
|1 |95 |106|heart murmur |Heart_Disease |
|1 |113 |126|echocardiogram |Test |
|1 |149 |158|March 1998 |Date |
|2 |185 |198|echocardiogram |Test |
|2 |220 |225|normal |Test_Result |
|2 |227 |251|left ventricular function |Test |
|2 |258 |280|left atrial enlargement |Heart_Disease |
|3 |327 |329|her |Gender |
|3 |346 |359|ACE inhibitors |Drug_Ingredient |
|3 |361 |365|10 mg |Strength |
|3 |368 |370|p.o |Route |
|3 |373 |377|daily |Frequency |
|4 |398 |407|Furosemide |Drug_Ingredient |
|4 |409 |413|40 mg |Strength |
|4 |416 |418|p.o |Route |
|4 |438 |440|she |Gender |
+-----------+-----+---+--------------------------+-------------------+
# assertion
+-----------+-----+---+-------------------------+---------------+----------------+
|sentence_id|begin|end|entity |label |assertion_status|
+-----------+-----+---+-------------------------+---------------+----------------+
|1 |95 |106|heart murmur |Heart_Disease |Past |
|1 |113 |126|echocardiogram |Test |Past |
|2 |185 |198|echocardiogram |Test |Present |
|2 |220 |225|normal |Test_Result |Present |
|2 |227 |251|left ventricular function|Test |Present |
|2 |258 |280|left atrial enlargement |Heart_Disease |Present |
|3 |346 |359|ACE inhibitors |Drug_Ingredient|Planned |
|4 |398 |407|Furosemide |Drug_Ingredient|Planned |
+-----------+-----+---+-------------------------+---------------+----------------+
# relation
+-----------+-------------------------+---------------------+-------------------------+---------------------+-------------------------+--------------------------+----------------------------------+---------------------------------+----------------------------------+---------------------------------+-------------------------+--------------------------+-------------------------+--------------------------+-------------------------+
|sentence_id|all_relations |all_relations_entity1|all_relations_chunk1 |all_relations_entity2|all_relations_chunk2 |test_result_date_relations|test_result_date_relations_entity1|test_result_date_relations_chunk1|test_result_date_relations_entity2|test_result_date_relations_chunk2|posology_relations |posology_relations_entity1|posology_relations_chunk1|posology_relations_entity2|posology_relations_chunk2|
+-----------+-------------------------+---------------------+-------------------------+---------------------+-------------------------+--------------------------+----------------------------------+---------------------------------+----------------------------------+---------------------------------+-------------------------+--------------------------+-------------------------+--------------------------+-------------------------+
|1 |is_finding_of |Heart_Disease |heart murmur |Test |echocardiogram |is_finding_of |Heart_Disease |heart murmur |Test |echocardiogram |Drug_Ingredient-Strength |Drug_Ingredient |ACE inhibitors |Strength |10 mg |
|1 |is_date_of |Heart_Disease |heart murmur |Date |March 1998 |is_date_of |Heart_Disease |heart murmur |Date |March 1998 |Drug_Ingredient-Route |Drug_Ingredient |ACE inhibitors |Route |p.o |
|1 |is_date_of |Test |echocardiogram |Date |March 1998 |is_date_of |Test |echocardiogram |Date |March 1998 |Drug_Ingredient-Frequency|Drug_Ingredient |ACE inhibitors |Frequency |daily |
|2 |is_finding_of |Test |echocardiogram |Heart_Disease |left atrial enlargement |is_finding_of |Test |echocardiogram |Heart_Disease |left atrial enlargement |Drug_Ingredient-Route |Drug_Ingredient |Furosemide |Route |p.o |
|2 |is_result_of |Test_Result |normal |Test |left ventricular function|is_result_of |Test_Result |normal |Test |left ventricular function |null |null |null |null |null |
|2 |is_finding_of |Test |left ventricular function|Heart_Disease |left atrial enlargement |is_finding_of |Test |left ventricular function |Heart_Disease |left atrial enlargement |null |null |null |null |null |
|3 |Drug_Ingredient-Strength |Drug_Ingredient |ACE inhibitors |Strength |10 mg |null |null |null |null |null |null |null |null |null |null |
|3 |Drug_Ingredient-Route |Drug_Ingredient |ACE inhibitors |Route |p.o |null |null |null |null |null |null |null |null |null |null |
|3 |Drug_Ingredient-Frequency|Drug_Ingredient |ACE inhibitors |Frequency |daily |null |null |null |null |null |null |null |null |null |null |
|4 |Drug_Ingredient-Strength |Drug_Ingredient |Furosemide |Strength |40 mg |null |null |null |null |null |null |null |null |null |null |
|4 |Drug_Ingredient-Route |Drug_Ingredient |Furosemide |Route |p.o |null |null |null |null |null |null |null |null |null |null |
+-----------+-------------------------+---------------------+-------------------------+---------------------+-------------------------+--------------------------+----------------------------------+---------------------------------+----------------------------------+---------------------------------+-------------------------+--------------------------+-------------------------+--------------------------+-------------------------+
Model Information
Model Name: | explain_clinical_doc_granular |
Type: | pipeline |
Compatibility: | Healthcare NLP 5.2.1+ |
License: | Licensed |
Edition: | Official |
Language: | en |
Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- NerConverterInternalModel
- AssertionDLModel
- PerceptronModel
- DependencyParserModel
- RelationExtractionModel
- PosologyREModel
- AnnotationMerger