Description
Pretrained named entity recognition deep learning model for clinical terminology. This model is trained with BertForTokenClassification method from transformers library and imported into Spark NLP. It detects 77 entities.
Definitions of Predicted Entities:
Medical_Device: All mentions related to medical devices and supplies.Vital_Signs_Header: Identifies section headers that correspond to Vital Signs of a patient.Allergen: Allergen related extractions mentioned in the document.Drug_BrandName: Commercial labeling name chosen by the labeler or the drug manufacturer for a drug containing a single or multiple drug active ingredients.Clinical_Dept: Terms that indicate the medical and/or surgical departments.Symptom: All the symptoms mentioned in the document, of a patient or someone else.External_body_part_or_region: All mentions related to external body parts or organs that can be examined by naked eye.Admission_Discharge: Terms that indicate the admission and/or the discharge of a patient.Age: All mention of ages, past or present, related to the patient or with anybody else.Birth_Entity: Mentions that indicate giving birth.Oncological: All the cancer, tumor or metastasis related extractions mentioned in the document, of the patient or someone else.Substance_Quantity: All mentions of substance quantity (quantitative information related to illicit/recreational drugs).Test_Result: Terms related to all the test results present in the document (clinical tests results are included).Test: Mentions of laboratory, pathology, and radiological tests.Procedure: All mentions of invasive medical or surgical procedures or treatments.Treatment: Includes therapeutic and minimally invasive treatment and procedures (invasive treatments or procedures are extracted as “Procedure”).Disease_Syndrome_Disorder: All the diseases mentioned in the document, of the patient or someone else (excluding diseases that are extracted with their specific labels, such as “Heart_Disease” etc.).
Predicted Entities
Medical_Device, Physical_Measurement, Alergen, Procedure, Substance_Quantity, Drug, Test_Result, Pregnancy_Newborn, Admission_Discharge, Demographics, Lifestyle, Header, Date_Time, Treatment, Clinical_Dept, Test, Death_Entity, Age, Oncological, Body_Part, Birth_Entity, Vital_Sign, Symptom, Disease_Syndrome_Disorder
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")
tokenClassifier = MedicalBertForTokenClassifier.pretrained("bert_token_classifier_ner_jsl", "en", "clinical/models")\
.setInputCols(["token", "sentence"])\
.setOutputCol("ner")\
.setCaseSensitive(True)
ner_converter = NerConverter()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
tokenClassifier,
ner_converter])
sample_text = """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-girl 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."""
df = spark.createDataFrame([[sample_text]]).toDF("text")
result = pipeline.fit(df).transform(df)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val tokenClassifier = MedicalBertForTokenClassifier.pretrained("bert_token_classifier_ner_jsl", "en", "clinical/models")
.setInputCols(Array("token", "sentence"))
.setOutputCol("ner")
.setCaseSensitive(True)
val ner_converter = new NerConverter()
.setInputCols(Array("sentence","token","ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
sentenceDetector,
tokenizer,
tokenClassifier,
ner_converter))
val sample_text = 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-girl 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 = pipeline.fit(sample_text).transform(sample_text)
import nlu
nlu.load("en.classify.token_bert.ner_jsl").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-girl 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 male |Demographics |
|congestion |Symptom |
|mom |Demographics |
|yellow discharge |Symptom |
|nares |Body_Part |
|she |Demographics |
|mild problems with his breathing|Symptom |
|perioral cyanosis |Symptom |
|retractions |Symptom |
|One day ago |Date_Time |
|mom |Demographics |
|tactile temperature |Symptom |
|Tylenol |Drug |
|Baby-girl |Age |
|decreased p.o. intake |Symptom |
|His |Demographics |
|breast-feeding |Body_Part |
|his |Demographics |
|respiratory congestion |Symptom |
|He |Demographics |
|tired |Symptom |
|fussy |Symptom |
|over the past 2 days |Date_Time |
|albuterol |Drug |
|ER |Clinical_Dept|
|His |Demographics |
|urine output has |Symptom |
|decreased |Symptom |
|he |Demographics |
|he |Demographics |
|Mom |Demographics |
|diarrhea |Symptom |
|His |Demographics |
|bowel |Body_Part |
+--------------------------------+-------------+
Model Information
| Model Name: | bert_token_classifier_ner_jsl |
| Compatibility: | Healthcare NLP 3.3.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 404.5 MB |
| Case sensitive: | true |
| Max sentence length: | 256 |
Benchmarking
Benchmarking
label tp fp fn prec rec f1
B-Medical_Device 2696 444 282 0.8585987 0.9053055 0.8813337
I-Physical_Measurement 220 16 34 0.9322034 0.8661417 0.8979592
B-Procedure 1800 239 281 0.8827857 0.8649688 0.8737864
B-Drug 1865 218 237 0.8953432 0.8872502 0.8912784
I-Test_Result 289 203 292 0.5873983 0.4974182 0.5386766
I-Pregnancy_Newborn 150 41 104 0.7853403 0.5905512 0.6741573
B-Admission_Discharge 255 35 6 0.8793103 0.9770115 0.9255898
B-Demographics 4609 119 123 0.9748308 0.9740068 0.9744186
I-Lifestyle 71 49 20 0.5916666 0.7802198 0.6729857
B-Header 2463 53 122 0.9789348 0.9528046 0.965693
I-Date_Time 928 184 191 0.8345324 0.8293119 0.8319139
B-Test_Result 866 198 262 0.8139097 0.7677305 0.7901459
I-Treatment 114 37 46 0.7549669 0.7125 0.733119
B-Clinical_Dept 688 83 76 0.8923476 0.9005235 0.8964169
B-Test 1920 333 313 0.8521970 0.8598298 0.8559965
B-Death_Entity 36 9 2 0.8 0.9473684 0.8674699
B-Lifestyle 268 58 50 0.8220859 0.8427673 0.8322981
B-Date_Time 823 154 176 0.8423746 0.8238238 0.8329959
I-Age 136 34 49 0.8 0.7351351 0.7661972
I-Oncological 345 41 19 0.8937824 0.9478022 0.9199999
I-Body_Part 3717 720 424 0.8377282 0.8976093 0.8666356
B-Pregnancy_Newborn 153 51 104 0.75 0.5953307 0.6637744
B-Treatment 169 41 58 0.8047619 0.7444933 0.7734553
I-Procedure 2302 326 417 0.8759513 0.8466348 0.8610435
B-Birth_Entity 6 5 7 0.5454545 0.4615384 0.5
I-Vital_Sign 639 197 93 0.7643540 0.8729508 0.815051
I-Header 4451 111 216 0.9756685 0.9537176 0.9645682
I-Death_Entity 2 0 0 1 1 1
I-Clinical_Dept 621 54 39 0.92 0.9409091 0.9303371
I-Test 1593 378 353 0.8082192 0.8186022 0.8133775
B-Age 472 43 51 0.9165048 0.9024856 0.9094413
I-Symptom 4227 1271 1303 0.7688250 0.7643761 0.7665941
I-Demographics 321 53 53 0.8582887 0.8582887 0.8582887
B-Body_Part 6312 912 809 0.8737541 0.8863923 0.8800279
B-Physical_Measurement 91 10 17 0.9009901 0.8425926 0.8708134
B-Disease_Syndrome_Disorder 2817 336 433 0.8934348 0.8667692 0.8799001
B-Symptom 4522 830 747 0.8449178 0.8582274 0.8515206
I-Disease_Syndrome_Disorder 2814 386 530 0.879375 0.8415072 0.8600244
I-Drug 3737 612 517 0.859278 0.8784673 0.8687667
I-Medical_Device 1825 331 131 0.8464749 0.9330266 0.8876459
B-Oncological 276 28 27 0.9078947 0.9108911 0.9093904
B-Vital_Sign 429 97 79 0.8155893 0.8444882 0.8297872
Macro-average 62038 9340 9110 0.7678277 0.7648211 0.7663215
Micro-average 62038 9340 9110 0.8691473 0.8719570 0.87055