Description
Pretrained named entity recognition deep learning model for clinical terminology. The SparkNLP deep learning model (MedicalNerModel) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN.
Predicted Entities
Injury_or_Poisoning, Direction, Test, Admission_Discharge, Death_Entity, Relationship_Status, Duration, Hyperlipidemia, Respiration, Birth_Entity, Age, Family_History_Header, Labour_Delivery, BMI, Temperature, Alcohol, Kidney_Disease, Oncological, Medical_History_Header, Cerebrovascular_Disease, Oxygen_Therapy, O2_Saturation, Psychological_Condition, Heart_Disease, Employment, Obesity, Disease_Syndrome_Disorder, Pregnancy, ImagingFindings, Procedure, Medical_Device, Race_Ethnicity, Section_Header, Drug, Symptom, Treatment, Substance, Route, Blood_Pressure, Diet, External_body_part_or_region, LDL, VS_Finding, Allergen, EKG_Findings, Imaging_Technique, Triglycerides, RelativeTime, Gender, Pulse, Social_History_Header, Substance_Quantity, Diabetes, Modifier, Internal_organ_or_component, Clinical_Dept, Form, Strength, Fetus_NewBorn, RelativeDate, Height, Test_Result, Time, Frequency, Sexually_Active_or_Sexual_Orientation, Weight, Vaccine, Vital_Signs_Header, Communicable_Disease, Dosage, Hypertension, HDL, Overweight, Total_Cholesterol, Smoking, Date.
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_greedy_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_greedy_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.greedy").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_greedy_clinical |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
Trained on data gathered and manually annotated by John Snow Labs. https://www.johnsnowlabs.com/data/
Benchmarking
entity tp fp fn total precision recall f1
VS_Finding 229.0 56.0 34.0 263.0 0.8035 0.8707 0.8358
Direction 4009.0 479.0 403.0 4412.0 0.8933 0.9087 0.9009
Female_Reproducti... 2.0 1.0 3.0 5.0 0.6667 0.4 0.5
Respiration 80.0 9.0 14.0 94.0 0.8989 0.8511 0.8743
Cerebrovascular_D... 82.0 27.0 18.0 100.0 0.7523 0.82 0.7847
not 4.0 0.0 0.0 4.0 1.0 1.0 1.0
Family_History_He... 86.0 4.0 3.0 89.0 0.9556 0.9663 0.9609
Heart_Disease 469.0 76.0 83.0 552.0 0.8606 0.8496 0.8551
ImagingFindings 68.0 38.0 75.0 143.0 0.6415 0.4755 0.5462
RelativeTime 141.0 76.0 66.0 207.0 0.6498 0.6812 0.6651
Strength 720.0 49.0 58.0 778.0 0.9363 0.9254 0.9308
Smoking 117.0 8.0 6.0 123.0 0.936 0.9512 0.9435
Medical_Device 3584.0 730.0 359.0 3943.0 0.8308 0.909 0.8681
EKG_Findings 41.0 20.0 45.0 86.0 0.6721 0.4767 0.5578
Pulse 138.0 23.0 24.0 162.0 0.8571 0.8519 0.8545
Psychological_Con... 121.0 14.0 29.0 150.0 0.8963 0.8067 0.8491
Overweight 5.0 2.0 0.0 5.0 0.7143 1.0 0.8333
Triglycerides 3.0 0.0 0.0 3.0 1.0 1.0 1.0
Obesity 49.0 6.0 4.0 53.0 0.8909 0.9245 0.9074
Admission_Discharge 325.0 30.0 2.0 327.0 0.9155 0.9939 0.9531
HDL 2.0 1.0 1.0 3.0 0.6667 0.6667 0.6667
Diabetes 118.0 13.0 7.0 125.0 0.9008 0.944 0.9219
Section_Header 3778.0 148.0 138.0 3916.0 0.9623 0.9648 0.9635
Age 617.0 52.0 47.0 664.0 0.9223 0.9292 0.9257
O2_Saturation 34.0 11.0 19.0 53.0 0.7556 0.6415 0.6939
Kidney_Disease 114.0 5.0 12.0 126.0 0.958 0.9048 0.9306
Test 2668.0 526.0 498.0 3166.0 0.8353 0.8427 0.839
Communicable_Disease 25.0 12.0 9.0 34.0 0.6757 0.7353 0.7042
Hypertension 152.0 10.0 6.0 158.0 0.9383 0.962 0.95
External_body_par... 652.0 387.0 340.0 2992.0 0.8727 0.8864 0.8795
Oxygen_Therapy 67.0 21.0 23.0 90.0 0.7614 0.7444 0.7528
Test_Result 1124.0 227.0 258.0 1382.0 0.832 0.8133 0.8225
Modifier 539.0 185.0 309.0 848.0 0.7445 0.6356 0.6858
BMI 7.0 1.0 1.0 8.0 0.875 0.875 0.875
Labour_Delivery 75.0 19.0 23.0 98.0 0.7979 0.7653 0.7813
Employment 249.0 51.0 57.0 306.0 0.83 0.8137 0.8218
Clinical_Dept 948.0 95.0 80.0 1028.0 0.9089 0.9222 0.9155
Time 36.0 7.0 7.0 43.0 0.8372 0.8372 0.8372
Procedure 3180.0 460.0 480.0 3660.0 0.8736 0.8689 0.8712
Diet 50.0 29.0 30.0 80.0 0.6329 0.625 0.6289
Oncological 478.0 46.0 50.0 528.0 0.9122 0.9053 0.9087
LDL 3.0 0.0 2.0 5.0 1.0 0.6 0.75
Symptom 6801.0 1097.0 1097.0 7898.0 0.8611 0.8611 0.8611
Temperature 109.0 12.0 7.0 116.0 0.9008 0.9397 0.9198
Vital_Signs_Header 213.0 27.0 16.0 229.0 0.8875 0.9301 0.9083
Relationship_Status 42.0 2.0 1.0 43.0 0.9545 0.9767 0.9655
Total_Cholesterol 10.0 4.0 5.0 15.0 0.7143 0.6667 0.6897
Blood_Pressure 167.0 22.0 23.0 190.0 0.8836 0.8789 0.8813
Injury_or_Poisoning 510.0 83.0 111.0 621.0 0.86 0.8213 0.8402
Drug_Ingredient 1698.0 160.0 158.0 1856.0 0.9139 0.9149 0.9144
Treatment 156.0 40.0 54.0 210.0 0.7959 0.7429 0.7685
Assertion_SocialD... 4.0 0.0 6.0 10.0 1.0 0.4 0.5714
Pregnancy 100.0 45.0 41.0 141.0 0.6897 0.7092 0.6993
Vaccine 13.0 3.0 6.0 19.0 0.8125 0.6842 0.7429
Disease_Syndrome_... 2861.0 452.0 376.0 3237.0 0.8636 0.8838 0.8736
Height 25.0 8.0 9.0 34.0 0.7576 0.7353 0.7463
Frequency 650.0 157.0 148.0 798.0 0.8055 0.8145 0.81
Route 872.0 83.0 85.0 957.0 0.9131 0.9112 0.9121
Death_Entity 49.0 7.0 6.0 55.0 0.875 0.8909 0.8829
Duration 367.0 132.0 95.0 462.0 0.7355 0.7944 0.7638
Internal_organ_or... 6532.0 1016.0 987.0 7519.0 0.8654 0.8687 0.8671
Alcohol 79.0 20.0 12.0 91.0 0.798 0.8681 0.8316
Date 515.0 19.0 19.0 534.0 0.9644 0.9644 0.9644
Hyperlipidemia 47.0 2.0 1.0 48.0 0.9592 0.9792 0.9691
Social_History_He... 89.0 9.0 4.0 93.0 0.9082 0.957 0.9319
Race_Ethnicity 113.0 0.0 3.0 116.0 1.0 0.9741 0.9869
Imaging_Technique 47.0 31.0 30.0 77.0 0.6026 0.6104 0.6065
Drug_BrandName 963.0 72.0 79.0 1042.0 0.9304 0.9242 0.9273
RelativeDate 553.0 128.0 121.0 674.0 0.812 0.8205 0.8162
Gender 6043.0 59.0 87.0 6130.0 0.9903 0.9858 0.9881
Form 227.0 35.0 47.0 274.0 0.8664 0.8285 0.847
Dosage 279.0 42.0 62.0 341.0 0.8692 0.8182 0.8429
Medical_History_H... 117.0 4.0 11.0 128.0 0.9669 0.9141 0.9398
Substance 59.0 16.0 16.0 75.0 0.7867 0.7867 0.7867
Weight 85.0 19.0 21.0 106.0 0.8173 0.8019 0.8095
macro - - - - - - 0.7286
micro - - - - - - 0.8715