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.
Definitions of Predicted Entities:
Symptom: All the symptoms mentioned in the document, of a patient or someone else.Pulse: Peripheral heart rate, without advanced information like measurement location.Death_Entity: Mentions that indicate the death of a patient.Age: All mention of ages, past or present, related to the patient or with anybody else.Modifier: Terms that modify the symptoms, diseases or risk factors. If a modifier is included in ICD-10 name of a specific disease, the respective modifier is not extracted separately.Substance: All mentions of substance use related to the patient or someone else (recreational drugs, illicit drugs).Drug_Ingredient: Active ingredient/s found in drug products.Weight: All mentions related to a patients weight.Drug_BrandName: Commercial labeling name chosen by the labeler or the drug manufacturer for a drug containing a single or multiple drug active ingredients.Procedure: All mentions of invasive medical or surgical procedures or treatments.Blood_Pressure: Systemic blood pressure, mean arterial pressure, systolic and/or diastolic are extracted.Gender: Gender-specific nouns and pronouns.Temperature: All mentions that refer to body temperature.Section_Header: All the section headers present in the text (Medical History, Family History, Social History, Physical Examination and Vital signs Headers are extracted separately with their specific labels).Route: Drug and medication administration routes available described by FDA.O2_Saturation: Systemic arterial, venous or peripheral oxygen saturation measurements.Respiration: Number of breaths per minute.Procedure: All mentions of invasive medical or surgical procedures or treatments.Frequency: Frequency of administration for a dose prescribed.Dosage: Quantity prescribed by the physician for an active ingredient; measurement units are available described by FDA.Allergen: Allergen related extractions mentioned in the document.
Predicted Entities
Diagnosis , Procedure_Name , Lab_Result , Procedure , Procedure_Findings , O2_Saturation , Procedure_incident_description , Dosage , Causative_Agents_(Virus_and_Bacteria) , Name , Cause_of_death , Substance_Name , Weight , Symptom_Name , Maybe , Modifier , Blood_Pressure , Frequency , Gender , Drug_incident_description , Age , Drug_Name , Temperature , Section_Name , Route , Negation , Negated , Allergenic_substance , Lab_Name , Respiratory_Rate
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("ner_jsl", "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("ner_jsl", "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").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 |
+---------------------------+------------+
|21-day-old |Age |
|male |Gender |
|congestion |Symptom_Name|
|mom |Gender |
|suctioning yellow discharge|Symptom_Name|
|she |Gender |
|mild |Modifier |
|problems with his breathing|Symptom_Name|
|negative |Negated |
|perioral cyanosis |Symptom_Name|
|retractions |Symptom_Name|
|mom |Gender |
|Tylenol |Drug_Name |
|His |Gender |
|his |Gender |
|respiratory congestion |Symptom_Name|
|He |Gender |
|tired |Symptom_Name|
|fussy |Symptom_Name|
|albuterol |Drug_Name |
+---------------------------+------------+
Model Information
| Model Name: | ner_jsl |
| 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
label tp fp fn prec rec f1
B-Pulse_Rate 77 39 12 0.663793 0.865169 0.75122
I-Diagnosis 2134 1139 1329 0.652001 0.616229 0.63361
I-Procedure_Name 2335 1329 956 0.637282 0.709511 0.671459
B-Lab_Result 601 182 94 0.767561 0.864748 0.813261
B-Procedure 1 0 5 1 0.166667 0.285714
B-Procedure_Findings 2 13 72 0.133333 0.027027 0.044944
B-O2_Saturation 1 3 4 0.25 0.2 0.222222
B-Dosage 477 197 68 0.707715 0.875229 0.782609
I-Causative_Agents_(Virus_and_Bacteria) 12 2 7 0.857143 0.631579 0.727273
B-Name 562 268 554 0.677108 0.503584 0.577595
I-Cause_of_death 9 5 11 0.642857 0.45 0.529412
I-Substance_Name 24 34 54 0.413793 0.307692 0.352941
I-Name 716 377 710 0.655078 0.502104 0.56848
B-Cause_of_death 9 6 8 0.6 0.529412 0.5625
B-Weight 52 22 9 0.702703 0.852459 0.77037
B-Symptom_Name 4364 1916 1652 0.694904 0.725399 0.709824
I-Maybe 27 51 61 0.346154 0.306818 0.325301
I-Symptom_Name 2073 1492 2348 0.581487 0.468898 0.519159
B-Modifier 1573 890 768 0.638652 0.671935 0.654871
B-Blood_Pressure 76 19 13 0.8 0.853933 0.826087
B-Frequency 308 134 77 0.696833 0.8 0.744861
I-Gender 26 31 28 0.45614 0.481482 0.468468
I-Drug_incident_description 4 10 57 0.285714 0.0655738 0.106667
B-Drug_incident_description 2 5 23 0.285714 0.08 0.125
I-Age 5 0 9 1 0.357143 0.526316
B-Drug_Name 1741 490 290 0.780368 0.857213 0.816987
B-Substance_Name 148 41 48 0.783069 0.755102 0.768831
B-Temperature 56 23 13 0.708861 0.811594 0.756757
I-Procedure 1 0 7 1 0.125 0.222222
B-Section_Name 2711 317 166 0.89531 0.942301 0.918205
I-Route 119 110 189 0.519651 0.386364 0.443203
B-Maybe 143 80 127 0.641256 0.52963 0.580122
B-Gender 5166 709 58 0.879319 0.988897 0.930895
I-Dosage 434 196 87 0.688889 0.833013 0.754127
B-Causative_Agents_(Virus_and_Bacteria) 19 3 8 0.863636 0.703704 0.77551
I-Frequency 275 134 191 0.672372 0.590129 0.628571
B-Age 357 27 16 0.929688 0.957105 0.943197
I-Lab_Result 45 78 152 0.365854 0.228426 0.28125
B-Negation 99 38 38 0.722628 0.722628 0.722628
B-Diagnosis 2786 1342 913 0.674903 0.753177 0.711895
I-Section_Name 3885 1353 179 0.741695 0.955955 0.835304
B-Route 421 217 166 0.659875 0.717206 0.687347
I-Negation 11 30 24 0.268293 0.314286 0.289474
B-Procedure_Name 1490 811 522 0.647545 0.740557 0.690934
B-Negated 1490 332 215 0.817783 0.8739 0.844911
I-Allergenic_substance 1 0 12 1 0.0769231 0.142857
I-Negated 89 132 146 0.402715 0.378723 0.390351
I-Procedure_Findings 2 31 283 0.0606061 0.00701754 0.012579
B-Allergenic_substance 72 29 24 0.712871 0.75 0.730965
I-Weight 47 35 16 0.573171 0.746032 0.648276
B-Lab_Name 804 290 122 0.734918 0.868251 0.79604
I-Modifier 99 73 422 0.575581 0.190019 0.285714
I-Temperature 1 0 14 1 0.0666667 0.125
I-Drug_Name 362 284 261 0.560372 0.581059 0.570528
I-Lab_Name 284 194 127 0.594142 0.690998 0.63892
B-Respiratory_Rate 46 5 5 0.901961 0.901961 0.901961
Macro-average 38674 15571 13819 0.589085 0.515426 0.5498
Micro-average 38674 15571 13819 0.712951 0.736746 0.724653