Description
Important Note: This model is trained with a partial dataset that is used to train ner_jsl; and meant to be used for benchmarking run at LLMs Healthcare Benchmarks.
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, Vaccine_Name, Vital_Signs_Header, Communicable_Disease, Dosage, Overweight, Hypertension, HDL, Total_Cholesterol, Smoking, Date
Live Demo Open in Colab Download 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")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
ner = MedicalNerModel.pretrained("ner_jsl_limited_80p_for_benchmarks", "en", "clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverterInternal() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")
ner_pipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
word_embeddings,
ner,
ner_converter])
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). Additionally, there is no side effect observed after Influenza vaccine. 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.
"""]]).toDF("text")
result = ner_pipeline.fit(data).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_limited_80p_for_benchmarks", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("jsl_ner")
val jsl_ner_converter = new NerConverterInternal()
.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). Additionally, there is no side effect observed after Influenza vaccine. 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.""").toDS.toDF("text")
val result = jsl_ner_pipeline.fit(data).transform(data)
Results
| | chunks | begin | end | sentence_id | entities |
|---:|:------------------------------------------|--------:|------:|--------------:|:-----------------------------|
| 0 | 21-day-old | 18 | 27 | 0 | Age |
| 1 | Caucasian | 29 | 37 | 0 | Race_Ethnicity |
| 2 | male | 39 | 42 | 0 | Gender |
| 3 | 2 days | 53 | 58 | 0 | Duration |
| 4 | congestion | 63 | 72 | 0 | Symptom |
| 5 | mom | 76 | 78 | 0 | Gender |
| 6 | suctioning yellow discharge | 89 | 115 | 0 | Symptom |
| 7 | nares | 136 | 140 | 0 | External_body_part_or_region |
| 8 | she | 148 | 150 | 0 | Gender |
| 9 | mild | 169 | 172 | 0 | Modifier |
| 10 | problems with his breathing while feeding | 174 | 214 | 0 | Symptom |
| 11 | perioral cyanosis | 238 | 254 | 0 | Symptom |
| 12 | retractions | 259 | 269 | 0 | Symptom |
| 13 | Influenza vaccine | 326 | 342 | 1 | Vaccine_Name |
| 14 | One day ago | 345 | 355 | 2 | RelativeDate |
| 15 | mom | 358 | 360 | 2 | Gender |
| 16 | tactile temperature | 377 | 395 | 2 | Symptom |
| 17 | Tylenol | 418 | 424 | 2 | Drug_BrandName |
| 18 | Baby | 427 | 430 | 3 | Age |
| 19 | decreased p.o | 450 | 462 | 3 | Symptom |
Model Information
| Model Name: | ner_jsl_limited_80p_for_benchmarks |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 15.3 MB |
References
Trained on data gathered and manually annotated by John Snow Labs. https://www.johnsnowlabs.com/data/
Benchmarking
label tp fp fn total precision recall f1
VS_Finding 164.0 69.0 40.0 204.0 0.7039 0.8039 0.7506
Direction 3394.0 452.0 367.0 3761.0 0.8825 0.9024 0.8923
Respiration 74.0 3.0 5.0 79.0 0.961 0.9367 0.9487
Cerebrovascular_D... 103.0 27.0 14.0 117.0 0.7923 0.8803 0.834
Family_History_He... 80.0 1.0 1.0 81.0 0.9877 0.9877 0.9877
Heart_Disease 432.0 74.0 64.0 496.0 0.8538 0.871 0.8623
ImagingFindings 66.0 32.0 102.0 168.0 0.6735 0.3929 0.4962
RelativeTime 103.0 55.0 82.0 185.0 0.6519 0.5568 0.6006
Strength 552.0 56.0 37.0 589.0 0.9079 0.9372 0.9223
Smoking 105.0 3.0 9.0 114.0 0.9722 0.9211 0.9459
Medical_Device 3043.0 530.0 364.0 3407.0 0.8517 0.8932 0.8719
Allergen 1.0 1.0 17.0 18.0 0.5 0.0556 0.1
EKG_Findings 42.0 36.0 53.0 95.0 0.5385 0.4421 0.4855
Pulse 106.0 23.0 17.0 123.0 0.8217 0.8618 0.8413
Psychological_Con... 103.0 35.0 20.0 123.0 0.7464 0.8374 0.7893
Overweight 5.0 3.0 0.0 5.0 0.625 1.0 0.7692
Triglycerides 3.0 0.0 2.0 5.0 1.0 0.6 0.75
Obesity 34.0 4.0 7.0 41.0 0.8947 0.8293 0.8608
Admission_Discharge 283.0 30.0 7.0 290.0 0.9042 0.9759 0.9386
HDL 2.0 1.0 0.0 2.0 0.6667 1.0 0.8
Diabetes 107.0 8.0 3.0 110.0 0.9304 0.9727 0.9511
Section_Header 3184.0 185.0 118.0 3302.0 0.9451 0.9643 0.9546
Age 524.0 49.0 61.0 585.0 0.9145 0.8957 0.905
O2_Saturation 29.0 10.0 13.0 42.0 0.7436 0.6905 0.716
Kidney_Disease 82.0 8.0 17.0 99.0 0.9111 0.8283 0.8677
Test 2063.0 451.0 414.0 2477.0 0.8206 0.8329 0.8267
Communicable_Disease 22.0 9.0 9.0 31.0 0.7097 0.7097 0.7097
Hypertension 124.0 4.0 7.0 131.0 0.9688 0.9466 0.9575
External_body_par... 2277.0 405.0 353.0 2630.0 0.849 0.8658 0.8573
Oxygen_Therapy 70.0 17.0 10.0 80.0 0.8046 0.875 0.8383
Modifier 1960.0 357.0 549.0 2509.0 0.8459 0.7812 0.8123
Test_Result 796.0 178.0 210.0 1006.0 0.8172 0.7913 0.804
BMI 4.0 0.0 1.0 5.0 1.0 0.8 0.8889
Labour_Delivery 55.0 31.0 26.0 81.0 0.6395 0.679 0.6587
Employment 192.0 24.0 48.0 240.0 0.8889 0.8 0.8421
Fetus_NewBorn 24.0 19.0 43.0 67.0 0.5581 0.3582 0.4364
Clinical_Dept 795.0 57.0 85.0 880.0 0.9331 0.9034 0.918
Time 22.0 11.0 9.0 31.0 0.6667 0.7097 0.6875
Procedure 2458.0 413.0 503.0 2961.0 0.8561 0.8301 0.8429
Diet 21.0 6.0 30.0 51.0 0.7778 0.4118 0.5385
Oncological 342.0 62.0 77.0 419.0 0.8465 0.8162 0.8311
LDL 4.0 0.0 0.0 4.0 1.0 1.0 1.0
Symptom 5777.0 1069.0 1277.0 7054.0 0.8439 0.819 0.8312
Temperature 86.0 5.0 12.0 98.0 0.9451 0.8776 0.9101
Vital_Signs_Header 201.0 23.0 14.0 215.0 0.8973 0.9349 0.9157
Relationship_Status 44.0 1.0 3.0 47.0 0.9778 0.9362 0.9565
Total_Cholesterol 10.0 5.0 7.0 17.0 0.6667 0.5882 0.625
Blood_Pressure 131.0 35.0 23.0 154.0 0.7892 0.8506 0.8188
Injury_or_Poisoning 431.0 71.0 140.0 571.0 0.8586 0.7548 0.8034
Drug_Ingredient 1508.0 106.0 158.0 1666.0 0.9343 0.9052 0.9195
Treatment 124.0 36.0 68.0 192.0 0.775 0.6458 0.7045
Pregnancy 89.0 40.0 38.0 127.0 0.6899 0.7008 0.6953
Vaccine 1.0 0.0 4.0 5.0 1.0 0.2 0.3333
Disease_Syndrome_... 2471.0 551.0 432.0 2903.0 0.8177 0.8512 0.8341
Height 12.0 3.0 9.0 21.0 0.8 0.5714 0.6667
Frequency 500.0 103.0 110.0 610.0 0.8292 0.8197 0.8244
Route 797.0 77.0 70.0 867.0 0.9119 0.9193 0.9156
Duration 258.0 52.0 106.0 364.0 0.8323 0.7088 0.7656
Death_Entity 38.0 7.0 3.0 41.0 0.8444 0.9268 0.8837
Internal_organ_or... 5434.0 839.0 984.0 6418.0 0.8663 0.8467 0.8564
Vaccine_Name 5.0 1.0 3.0 8.0 0.8333 0.625 0.7143
Alcohol 78.0 13.0 6.0 84.0 0.8571 0.9286 0.8914
Substance_Quantity 0.0 9.0 1.0 1.0 0.0 0.0 0.0
Date 455.0 25.0 12.0 467.0 0.9479 0.9743 0.9609
Hyperlipidemia 34.0 0.0 2.0 36.0 1.0 0.9444 0.9714
Social_History_He... 75.0 3.0 3.0 78.0 0.9615 0.9615 0.9615
Imaging_Technique 25.0 16.0 23.0 48.0 0.6098 0.5208 0.5618
Race_Ethnicity 110.0 0.0 2.0 112.0 1.0 0.9821 0.991
Drug_BrandName 788.0 65.0 53.0 841.0 0.9238 0.937 0.9303
RelativeDate 488.0 150.0 98.0 586.0 0.7649 0.8328 0.7974
Gender 5189.0 66.0 55.0 5244.0 0.9874 0.9895 0.9885
Dosage 229.0 32.0 69.0 298.0 0.8774 0.7685 0.8193
Form 179.0 22.0 37.0 216.0 0.8905 0.8287 0.8585
Medical_History_H... 112.0 7.0 6.0 118.0 0.9412 0.9492 0.9451
Birth_Entity 2.0 2.0 5.0 7.0 0.5 0.2857 0.3636
Substance 60.0 6.0 11.0 71.0 0.9091 0.8451 0.8759
Sexually_Active_o... 2.0 1.0 1.0 3.0 0.6667 0.6667 0.6667
Weight 77.0 8.0 12.0 89.0 0.9059 0.8652 0.8851
macro - - - - - - 0.7914
micro - - - - - - 0.8691