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. This model is the official version of jsl_ner_wip_clinical model.
Definitions of Predicted Entities:
Injury_or_Poisoning: Physical harm or injury caused to the body, including those caused by accidents, falls, or poisoning of a patient or someone else.Direction: All the information relating to the laterality of the internal and external organs.Test: Mentions of laboratory, pathology, and radiological tests.Admission_Discharge: Terms that indicate the admission and/or the discharge of a patient.Death_Entity: Mentions that indicate the death of a patient.Relationship_Status: State of patients romantic or social relationships (e.g. single, married, divorced).Duration: The duration of a medical treatment or medication use.Respiration: Number of breaths per minute.Hyperlipidemia: Terms that indicate hyperlipidemia with relevant subtypes and synonims.Birth_Entity: Mentions that indicate giving birth.Age: All mention of ages, past or present, related to the patient or with anybody else.Labour_Delivery: Extractions include stages of labor and delivery.Family_History_Header: identifies section headers that correspond to Family History of the patient.BMI: Numeric values and other text information related to Body Mass Index.Temperature: All mentions that refer to body temperature.Alcohol: Terms that indicate alcohol use, abuse or drinking issues of a patient or someone else.Kidney_Disease: Terms that refer to any kidney diseases (includes mentions of modifiers such as “Acute” or “Chronic”).Oncological: All the cancer, tumor or metastasis related extractions mentioned in the document, of the patient or someone else.Medical_History_Header: Identifies section headers that correspond to Past Medical History of a patient.Cerebrovascular_Disease: All terms that refer to cerebrovascular diseases and events.Oxygen_Therapy: Breathing support triggered by patient or entirely or partially by machine (e.g. ventilator, BPAP, CPAP).O2_Saturation: Systemic arterial, venous or peripheral oxygen saturation measurements.Psychological_Condition: All the Mental health diagnosis, disorders, conditions or syndromes of a patient or someone else.Heart_Disease: All mentions of acquired, congenital or degenerative heart diseases.Employment: All mentions of patient or provider occupational titles and employment status .Obesity: Terms related to a patient being obese (overweight and BMI are extracted as different labels).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.).Pregnancy: All terms related to Pregnancy (excluding terms that are extracted with their specific labels, such as “Labour_Delivery” etc.).ImagingFindings: All mentions of radiographic and imagistic findings.Procedure: All mentions of invasive medical or surgical procedures or treatments.Medical_Device: All mentions related to medical devices and supplies.Race_Ethnicity: All terms that refer to racial and national origin of sociocultural groups.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).Symptom: All the symptoms mentioned in the document, of a patient or someone else.Treatment: Includes therapeutic and minimally invasive treatment and procedures (invasive treatments or procedures are extracted as “Procedure”).Substance: All mentions of substance use related to the patient or someone else (recreational drugs, illicit drugs).Route: Drug and medication administration routes available described by FDA.Drug_Ingredient: Active ingredient/s found in drug products.Blood_Pressure: Systemic blood pressure, mean arterial pressure, systolic and/or diastolic are extracted.Diet: All mentions and information regarding patients dietary habits.External_body_part_or_region: All mentions related to external body parts or organs that can be examined by naked eye.LDL: All mentions related to the lab test and results for LDL (Low Density Lipoprotein).VS_Finding: Qualitative data (e.g. Fever, Cyanosis, Tachycardia) and any other symptoms that refers to vital signs.Allergen: Allergen related extractions mentioned in the document.EKG_Findings: All mentions of EKG readings.Imaging_Technique: All mentions of special radiographic views or special imaging techniques used in radiology.Triglycerides: All mentions terms related to specific lab test for Triglycerides.RelativeTime: Time references that are relative to different times or events (e.g. words such as “approximately”, “in the morning”).Gender: Gender-specific nouns and pronouns.Pulse: Peripheral heart rate, without advanced information like measurement location.Social_History_Header: Identifies section headers that correspond to Social History of a patient.Substance_Quantity: All mentions of substance quantity (quantitative information related to illicit/recreational drugs).Diabetes: All terms related to diabetes mellitus.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.Internal_organ_or_component: All mentions related to internal body parts or organs that can not be examined by naked eye.Clinical_Dept: Terms that indicate the medical and/or surgical departments.Form: Drug and medication forms available described by FDA.Drug_BrandName: Commercial labeling name chosen by the labeler or the drug manufacturer for a drug containing a single or multiple drug active ingredients.Strength: Potency of one unit of drug (or a combination of drugs) the measurement units available are described by FDA.Fetus_NewBorn: All terms related to fetus, infant, new born (excluding terms that are extracted with their specific labels, such as “Labour_Delivery”, “Pregnancy” etc.).RelativeDate: Temporal references that are relative to the date of the text or to any other specific date (e.g. “approximately two years ago”, “about two days ago”).Height: All mentions related to a patients height.Test_Result: Terms related to all the test results present in the document (clinical tests results are included).Sexually_Active_or_Sexual_Orientation: All terms that are related to sexuality, sexual orientations and sexual activity.Frequency: Frequency of administration for a dose prescribed.Time: Specific time references (hour and/or minutes).Weight: All mentions related to a patients weight.Vaccine: Generic and brand name of vaccines or vaccination procedure.Vital_Signs_Header: Identifies section headers that correspond to Vital Signs of a patient.Communicable_Disease: Includes all mentions of communicable diseases.Dosage: Quantity prescribed by the physician for an active ingredient; measurement units are available described by FDA.Overweight: Terms related to the patient being overweight (BMI and Obesity is extracted separately).Hypertension: All terms related to Hypertension (quantitative data such as 150/100 is extracted as Blood_Pressure).HDL: Terms related to the lab test for HDL (High Density Lipoprotein).Total_Cholesterol: Terms related to the lab test and results for cholesterol.Smoking: All mentions of smoking status of a patient.Date: Mentions of an exact date, in any format, including day number, month and/or year.
Predicted Entities
Injury_or_Poisoning, Direction, Test, Admission_Discharge, Death_Entity, Relationship_Status, Duration, Respiration, Hyperlipidemia, Birth_Entity, Age, Labour_Delivery, Family_History_Header, 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, Symptom, Treatment, Substance, Route, Drug_Ingredient, 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, Drug_BrandName, Strength, Fetus_NewBorn, RelativeDate, Height, Test_Result, Sexually_Active_or_Sexual_Orientation, Frequency, Time, Weight, Vaccine, Vital_Signs_Header, Communicable_Disease, Dosage, Overweight, Hypertension, HDL, 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("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_label |
+-----------------------------------------+----------------------------+
|21-day-old |Age |
|Caucasian |Race_Ethnicity |
|male |Gender |
|for 2 days |Duration |
|congestion |Symptom |
|mom |Gender |
|yellow |Modifier |
|discharge |Symptom |
|nares |External_body_part_or_region|
|she |Gender |
|mild |Modifier |
|problems with his breathing while feeding|Symptom |
|perioral cyanosis |Symptom |
|retractions |Symptom |
|One day ago |RelativeDate |
|mom |Gender |
|Tylenol |Drug_BrandName |
|Baby |Age |
|decreased p.o. intake |Symptom |
|His |Gender |
+-----------------------------------------+----------------------------+
Model Information
| Model Name: | ner_jsl |
| Compatibility: | Healthcare NLP 3.1.0+ |
| 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 235.0 46.0 43.0 278.0 0.8363 0.8453 0.8408
Direction 3972.0 465.0 458.0 4430.0 0.8952 0.8966 0.8959
Respiration 82.0 4.0 4.0 86.0 0.9535 0.9535 0.9535
Cerebrovascular_D... 93.0 20.0 24.0 117.0 0.823 0.7949 0.8087
Family_History_He... 88.0 6.0 3.0 91.0 0.9362 0.967 0.9514
Heart_Disease 447.0 82.0 119.0 566.0 0.845 0.7898 0.8164
RelativeTime 158.0 80.0 59.0 217.0 0.6639 0.7281 0.6945
Strength 624.0 58.0 53.0 677.0 0.915 0.9217 0.9183
Smoking 121.0 11.0 4.0 125.0 0.9167 0.968 0.9416
Medical_Device 3716.0 491.0 466.0 4182.0 0.8833 0.8886 0.8859
Pulse 136.0 22.0 14.0 150.0 0.8608 0.9067 0.8831
Psychological_Con... 135.0 9.0 29.0 164.0 0.9375 0.8232 0.8766
Overweight 2.0 1.0 0.0 2.0 0.6667 1.0 0.8
Triglycerides 3.0 0.0 2.0 5.0 1.0 0.6 0.75
Obesity 42.0 5.0 6.0 48.0 0.8936 0.875 0.8842
Admission_Discharge 318.0 24.0 11.0 329.0 0.9298 0.9666 0.9478
HDL 3.0 0.0 0.0 3.0 1.0 1.0 1.0
Diabetes 110.0 14.0 8.0 118.0 0.8871 0.9322 0.9091
Section_Header 3740.0 148.0 157.0 3897.0 0.9619 0.9597 0.9608
Age 627.0 75.0 48.0 675.0 0.8932 0.9289 0.9107
O2_Saturation 34.0 14.0 17.0 51.0 0.7083 0.6667 0.6869
Kidney_Disease 96.0 12.0 34.0 130.0 0.8889 0.7385 0.8067
Test 2504.0 545.0 498.0 3002.0 0.8213 0.8341 0.8276
Communicable_Disease 21.0 10.0 6.0 27.0 0.6774 0.7778 0.7241
Hypertension 162.0 5.0 10.0 172.0 0.9701 0.9419 0.9558
External_body_par... 2626.0 356.0 413.0 3039.0 0.8806 0.8641 0.8723
Oxygen_Therapy 81.0 15.0 14.0 95.0 0.8438 0.8526 0.8482
Modifier 2341.0 404.0 539.0 2880.0 0.8528 0.8128 0.8324
Test_Result 1007.0 214.0 255.0 1262.0 0.8247 0.7979 0.8111
BMI 9.0 1.0 0.0 9.0 0.9 1.0 0.9474
Labour_Delivery 57.0 23.0 33.0 90.0 0.7125 0.6333 0.6706
Employment 271.0 59.0 55.0 326.0 0.8212 0.8313 0.8262
Fetus_NewBorn 66.0 33.0 51.0 117.0 0.6667 0.5641 0.6111
Clinical_Dept 923.0 110.0 83.0 1006.0 0.8935 0.9175 0.9053
Time 29.0 13.0 16.0 45.0 0.6905 0.6444 0.6667
Procedure 3185.0 462.0 501.0 3686.0 0.8733 0.8641 0.8687
Diet 36.0 20.0 45.0 81.0 0.6429 0.4444 0.5255
Oncological 459.0 61.0 55.0 514.0 0.8827 0.893 0.8878
LDL 3.0 0.0 3.0 6.0 1.0 0.5 0.6667
Symptom 7104.0 1302.0 1200.0 8304.0 0.8451 0.8555 0.8503
Temperature 116.0 6.0 8.0 124.0 0.9508 0.9355 0.9431
Vital_Signs_Header 215.0 29.0 24.0 239.0 0.8811 0.8996 0.8903
Relationship_Status 49.0 2.0 1.0 50.0 0.9608 0.98 0.9703
Total_Cholesterol 11.0 4.0 5.0 16.0 0.7333 0.6875 0.7097
Blood_Pressure 158.0 18.0 22.0 180.0 0.8977 0.8778 0.8876
Injury_or_Poisoning 579.0 130.0 127.0 706.0 0.8166 0.8201 0.8184
Drug_Ingredient 1716.0 153.0 132.0 1848.0 0.9181 0.9286 0.9233
Treatment 136.0 36.0 60.0 196.0 0.7907 0.6939 0.7391
Pregnancy 123.0 36.0 51.0 174.0 0.7736 0.7069 0.7387
Vaccine 13.0 2.0 6.0 19.0 0.8667 0.6842 0.7647
Disease_Syndrome_... 2981.0 559.0 446.0 3427.0 0.8421 0.8699 0.8557
Height 30.0 10.0 15.0 45.0 0.75 0.6667 0.7059
Frequency 595.0 99.0 138.0 733.0 0.8573 0.8117 0.8339
Route 858.0 76.0 89.0 947.0 0.9186 0.906 0.9123
Duration 351.0 99.0 108.0 459.0 0.78 0.7647 0.7723
Death_Entity 43.0 14.0 5.0 48.0 0.7544 0.8958 0.819
Internal_organ_or... 6477.0 972.0 991.0 7468.0 0.8695 0.8673 0.8684
Alcohol 80.0 18.0 13.0 93.0 0.8163 0.8602 0.8377
Substance_Quantity 6.0 7.0 4.0 10.0 0.4615 0.6 0.5217
Date 498.0 38.0 19.0 517.0 0.9291 0.9632 0.9459
Hyperlipidemia 47.0 3.0 3.0 50.0 0.94 0.94 0.94
Social_History_He... 99.0 7.0 7.0 106.0 0.934 0.934 0.934
Race_Ethnicity 116.0 0.0 0.0 116.0 1.0 1.0 1.0
Imaging_Technique 40.0 18.0 47.0 87.0 0.6897 0.4598 0.5517
Drug_BrandName 859.0 62.0 61.0 920.0 0.9327 0.9337 0.9332
RelativeDate 566.0 124.0 143.0 709.0 0.8203 0.7983 0.8091
Gender 6096.0 80.0 101.0 6197.0 0.987 0.9837 0.9854
Dosage 244.0 31.0 57.0 301.0 0.8873 0.8106 0.8472
Form 234.0 32.0 55.0 289.0 0.8797 0.8097 0.8432
Medical_History_H... 114.0 9.0 10.0 124.0 0.9268 0.9194 0.9231
Birth_Entity 4.0 2.0 3.0 7.0 0.6667 0.5714 0.6154
Substance 59.0 8.0 11.0 70.0 0.8806 0.8429 0.8613
Sexually_Active_o... 5.0 3.0 4.0 9.0 0.625 0.5556 0.5882
Weight 90.0 10.0 21.0 111.0 0.9 0.8108 0.8531
macro - - - - - - 0.8148
micro - - - - - - 0.8788