Description
Detect modifiers and other clinical entities using pretrained NER model.
Predicted Entities
Kidney_Disease, Height, Family_History_Header, RelativeTime, Hypertension, HDL, Alcohol, Test, Substance, Fetus_NewBorn, Diet, Substance_Quantity, Allergen, Form, Birth_Entity, Age, Race_Ethnicity, Modifier, Internal_organ_or_component, Hyperlipidemia, ImagingFindings, Psychological_Condition, Triglycerides, Cerebrovascular_Disease, Obesity, Duration, Weight, Date, Test_Result, Strength, VS_Finding, Respiration, Social_History_Header, Employment, Injury_or_Poisoning, Medical_History_Header, Death_Entity, Relationship_Status, Oxygen_Therapy, Blood_Pressure, Gender, Section_Header, Oncological, Drug, Labour_Delivery, Heart_Disease, LDL, Medical_Device, Temperature, Treatment, Female_Reproductive_Status, Total_Cholesterol, Time, Disease_Syndrome_Disorder, Communicable_Disease, EKG_Findings, Diabetes, Route, External_body_part_or_region, Pulse, Vital_Signs_Header, Direction, Admission_Discharge, Overweight, RelativeDate, O2_Saturation, BMI, Vaccine, Pregnancy, Sexually_Active_or_Sexual_Orientation, Procedure, Frequency, Dosage, Symptom, Clinical_Dept, Smoking
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models") \
.setInputCols(["document"]) \
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
jsl_ner = MedicalNerModel.pretrained("jsl_ner_wip_modifier_clinical", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("jsl_ner")
jsl_ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "jsl_ner"]) \
.setOutputCol("ner_chunk")
jsl_ner_pipeline = Pipeline().setStages([
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
jsl_ner,
jsl_ner_converter])
jsl_ner_model = jsl_ner_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
data = spark.createDataFrame([["The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature."]]).toDF("text")
result = jsl_ner_model.transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val jsl_ner = MedicalNerModel.pretrained("jsl_ner_wip_modifier_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("jsl_ner")
val jsl_ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "jsl_ner"))
.setOutputCol("ner_chunk")
val jsl_ner_pipeline = new Pipeline().setStages(Array(
documentAssembler,
sentenceDetector,
tokenizer,
embeddings,
jsl_ner,
jsl_ner_converter))
val data = Seq("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""").toDS.toDF("text")
val result = jsl_ner_pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.jsl.wip.clinical.modifier").predict("""The patient is a 21-day-old Caucasian male here for 2 days of congestion - mom has been suctioning yellow discharge from the patient's nares, plus she has noticed some mild problems with his breathing while feeding (but negative for any perioral cyanosis or retractions). One day ago, mom also noticed a tactile temperature and gave the patient Tylenol. Baby also has had some decreased p.o. intake. His normal breast-feeding is down from 20 minutes q.2h. to 5 to 10 minutes secondary to his respiratory congestion. He sleeps well, but has been more tired and has been fussy over the past 2 days. The parents noticed no improvement with albuterol treatments given in the ER. His urine output has also decreased; normally he has 8 to 10 wet and 5 dirty diapers per 24 hours, now he has down to 4 wet diapers per 24 hours. Mom denies any diarrhea. His bowel movements are yellow colored and soft in nature.""")
Results
+----------------------------------------------+----------------------------+
|chunk |ner_label |
+----------------------------------------------+----------------------------+
|21-day-old |Age |
|Caucasian |Race_Ethnicity |
|male |Gender |
|for 2 days |Duration |
|congestion |Symptom |
|mom |Gender |
|suctioning yellow discharge |Symptom |
|nares |External_body_part_or_region|
|she |Gender |
|mild problems with his breathing while feeding|Symptom |
|perioral cyanosis |Symptom |
|retractions |Symptom |
|One day ago |RelativeDate |
|mom |Gender |
|tactile temperature |Symptom |
|Tylenol |Drug |
|Baby |Age |
|decreased p.o. intake |Symptom |
|His |Gender |
|20 minutes |Duration |
|q.2h. |Frequency |
|to 5 to 10 minutes |Duration |
|his |Gender |
|respiratory congestion |Symptom |
|He |Gender |
|tired |Symptom |
|fussy |Symptom |
|over the past 2 days |RelativeDate |
|albuterol |Drug |
|ER |Clinical_Dept |
|His |Gender |
|urine output has also decreased |Symptom |
|he |Gender |
|per 24 hours |Frequency |
|he |Gender |
|per 24 hours |Frequency |
|Mom |Gender |
|diarrhea |Symptom |
|His |Gender |
|bowel |Internal_organ_or_component |
+----------------------------------------------+----------------------------+
Model Information
|—|—| |Model Name:|jsl_ner_wip_modifier_clinical| |Compatibility:|Healthcare NLP 3.0.0+| |License:|Licensed| |Edition:|Official| |Input Labels:|[sentence, token, embeddings]| |Output Labels:|[ner]| |Language:|en|