Description
Pretrained named entity recognition deep learning model for clinical terminology. This model is capable of predicting up to 87 different entities and is based on ner_jsl.
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
Social_History_Header, Oncology_Therapy, Blood_Pressure, Respiration, Performance_Status, Family_History_Header, Dosage, Clinical_Dept, Diet, Procedure, HDL, Weight, Admission_Discharge, LDL, Kidney_Disease, Oncological, Route, Imaging_Technique, Puerperium, Overweight, Temperature, Diabetes, Vaccine, Age, Test_Result, Employment, Time, Obesity, EKG_Findings, Pregnancy, Communicable_Disease, BMI, Strength, Tumor_Finding, Section_Header, RelativeDate, ImagingFindings, Death_Entity, Date, Cerebrovascular_Disease, Treatment, Labour_Delivery, Pregnancy_Delivery_Puerperium, Direction, Internal_organ_or_component, Psychological_Condition, Form, Medical_Device, Test, Symptom, Disease_Syndrome_Disorder, Staging, Birth_Entity, Hyperlipidemia, O2_Saturation, Frequency, External_body_part_or_region, Drug_Ingredient, Vital_Signs_Header, Substance_Quantity, Race_Ethnicity, VS_Finding, Injury_or_Poisoning, Medical_History_Header, Alcohol, Triglycerides, Total_Cholesterol, Sexually_Active_or_Sexual_Orientation, Female_Reproductive_Status, Relationship_Status, Drug_BrandName, RelativeTime, Duration, Hypertension, Metastasis, Gender, Oxygen_Therapy, Pulse, Heart_Disease, Modifier, Allergen, Smoking, Substance, Cancer_Modifier, Fetus_NewBorn, Height
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_enriched", "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_enriched", "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.enriched").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 | begin | end | entity |
|---:|:------------------------------------------|--------:|------:|:-----------------------------|
| 0 | 21-day-old | 17 | 26 | Age |
| 1 | Caucasian | 28 | 36 | Race_Ethnicity |
| 2 | male | 38 | 41 | Gender |
| 3 | 2 days | 52 | 57 | Duration |
| 4 | congestion | 62 | 71 | Symptom |
| 5 | mom | 75 | 77 | Gender |
| 6 | suctioning yellow discharge | 88 | 114 | Symptom |
| 7 | nares | 135 | 139 | External_body_part_or_region |
| 8 | she | 147 | 149 | Gender |
| 9 | mild | 168 | 171 | Modifier |
| 10 | problems with his breathing while feeding | 173 | 213 | Symptom |
| 11 | perioral cyanosis | 237 | 253 | Symptom |
| 12 | retractions | 258 | 268 | Symptom |
| 13 | One day ago | 272 | 282 | RelativeDate |
| 14 | mom | 285 | 287 | Gender |
| 15 | tactile temperature | 304 | 322 | Symptom |
| 16 | Tylenol | 345 | 351 | Drug_BrandName |
| 17 | Baby | 354 | 357 | Age |
| 18 | decreased p.o. intake | 377 | 397 | Symptom |
| 19 | His | 400 | 402 | Gender |
| 20 | q.2h | 450 | 453 | Frequency |
| 21 | 5 to 10 minutes | 459 | 473 | Duration |
| 22 | his | 488 | 490 | Gender |
| 23 | respiratory congestion | 492 | 513 | Symptom |
| 24 | He | 516 | 517 | Gender |
| 25 | tired | 550 | 554 | Symptom |
| 26 | fussy | 569 | 573 | Symptom |
| 27 | over the past 2 days | 575 | 594 | RelativeDate |
| 28 | albuterol | 637 | 645 | Drug_Ingredient |
| 29 | ER | 671 | 672 | Clinical_Dept |
| 30 | His | 675 | 677 | Gender |
| 31 | urine output has also decreased | 679 | 709 | Symptom |
| 32 | he | 721 | 722 | Gender |
| 33 | per 24 hours | 760 | 771 | Frequency |
| 34 | he | 778 | 779 | Gender |
| 35 | per 24 hours | 807 | 818 | Frequency |
| 36 | Mom | 821 | 823 | Gender |
| 37 | diarrhea | 836 | 843 | Symptom |
| 38 | His | 846 | 848 | Gender |
| 39 | bowel | 850 | 854 | Internal_organ_or_component |
Model Information
| Model Name: | ner_jsl_enriched |
| Compatibility: | Healthcare NLP 3.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
Trained on data sampled from MTSamples and Clinicaltrials.gov, and annotated in-house.
Benchmarking
label tp fp fn prec rec f1
B-Oxygen_Therapy 139 44 44 0.75956285 0.75956285 0.75956285
B-Oncology_Therapy 2 0 4 1.0 0.33333334 0.5
B-Cerebrovascular_Disease 49 13 23 0.7903226 0.6805556 0.7313434
B-Triglycerides 3 0 1 1.0 0.75 0.85714287
I-Cerebrovascular_Disease 18 11 22 0.62068963 0.45 0.52173907
B-Medical_Device 2723 350 299 0.88610476 0.9010589 0.8935192
B-Labour_Delivery 38 6 27 0.8636364 0.5846154 0.6972478
I-Vaccine 27 2 8 0.9310345 0.7714286 0.84374994
I-Obesity 7 0 0 1.0 1.0 1.0
I-Smoking 2 3 4 0.4 0.33333334 0.36363637
B-RelativeTime 141 69 70 0.67142856 0.66824645 0.6698337
I-Staging 0 0 1 0.0 0.0 0.0
B-Imaging_Technique 23 7 30 0.76666665 0.43396226 0.55421686
B-Heart_Disease 264 54 51 0.8301887 0.83809525 0.8341232
B-Procedure 2091 206 277 0.9103178 0.8830236 0.89646304
I-RelativeTime 177 56 65 0.75965667 0.73140496 0.74526316
I-Substance_Quantity 0 12 1 0.0 0.0 0.0
B-Obesity 53 0 4 1.0 0.9298246 0.9636364
I-RelativeDate 702 94 97 0.88190955 0.8785983 0.8802508
B-O2_Saturation 55 27 22 0.6707317 0.71428573 0.6918239
B-Direction 3138 213 260 0.9364369 0.9234844 0.92991555
I-Alcohol 2 0 4 1.0 0.33333334 0.5
I-Oxygen_Therapy 104 60 57 0.63414633 0.6459627 0.64
B-Diet 34 5 39 0.8717949 0.46575344 0.6071429
B-Dosage 267 59 115 0.8190184 0.69895285 0.7542373
B-Injury_or_Poisoning 353 67 67 0.8404762 0.8404762 0.8404762
B-Hypertension 98 3 9 0.97029704 0.91588783 0.94230765
I-Test_Result 1093 58 145 0.94960904 0.8828756 0.91502714
B-Female_Reproductive_Status 0 0 1 0.0 0.0 0.0
B-Substance_Quantity 0 4 1 0.0 0.0 0.0
B-Alcohol 72 6 15 0.9230769 0.82758623 0.8727273
B-Height 14 7 8 0.6666667 0.6363636 0.65116286
I-Substance 19 2 4 0.9047619 0.82608694 0.86363643
B-RelativeDate 470 65 79 0.8785047 0.856102 0.86715865
B-Admission_Discharge 242 8 3 0.968 0.9877551 0.9777778
B-Date 424 25 18 0.94432074 0.959276 0.9517396
B-Kidney_Disease 71 12 12 0.85542166 0.85542166 0.85542166
I-Admission_Discharge 0 0 1 0.0 0.0 0.0
I-Strength 506 82 38 0.8605442 0.93014705 0.89399296
B-Allergen 0 3 10 0.0 0.0 0.0
I-Injury_or_Poisoning 315 83 93 0.7914573 0.77205884 0.7816377
I-Drug_Ingredient 300 88 46 0.77319586 0.867052 0.8174387
I-Time 298 31 14 0.90577507 0.9551282 0.9297972
B-Substance 54 7 9 0.8852459 0.85714287 0.87096775
B-Total_Cholesterol 12 2 3 0.85714287 0.8 0.82758623
I-Vital_Signs_Header 138 8 6 0.94520545 0.9583333 0.9517241
I-Internal_organ_or_component 2826 302 304 0.9034527 0.9028754 0.903164
B-Hyperlipidemia 27 1 1 0.96428573 0.96428573 0.9642857
I-Sexually_Active_or_Sexual_Orientation 4 2 1 0.6666667 0.8 0.72727275
B-Sexually_Active_or_Sexual_Orientation 4 3 2 0.5714286 0.6666667 0.61538464
I-Fetus_NewBorn 27 18 19 0.6 0.5869565 0.5934066
B-BMI 5 0 4 1.0 0.5555556 0.71428573
B-ImagingFindings 63 36 64 0.6363636 0.496063 0.5575221
B-Drug_Ingredient 1905 202 183 0.9041291 0.9123563 0.90822405
B-Test_Result 1327 131 184 0.9101509 0.87822634 0.8939037
B-Section_Header 2763 120 106 0.9583767 0.96305335 0.96070933
I-Treatment 103 40 39 0.7202797 0.7253521 0.72280705
B-Clinical_Dept 744 62 99 0.9230769 0.8825623 0.902365
I-Kidney_Disease 109 12 1 0.90082645 0.9909091 0.94372296
I-Pulse 156 35 27 0.8167539 0.852459 0.8342246
B-Test 2312 293 418 0.887524 0.84688646 0.8667291
B-Weight 64 10 14 0.8648649 0.82051283 0.8421053
I-Respiration 81 47 11 0.6328125 0.8804348 0.73636365
I-EKG_Findings 73 15 70 0.82954544 0.5104895 0.63203466
I-Section_Header 1999 73 97 0.96476835 0.95372134 0.9592131
I-VS_Finding 32 17 28 0.6530612 0.53333336 0.58715594
B-Strength 513 60 44 0.89528793 0.92100537 0.9079646
I-Cancer_Modifier 5 0 0 1.0 1.0 1.0
I-Social_History_Header 39 6 0 0.8666667 1.0 0.92857146
B-Vital_Signs_Header 216 14 6 0.9391304 0.972973 0.95575225
B-Death_Entity 41 11 3 0.78846157 0.9318182 0.8541667
B-Modifier 2050 335 307 0.8595388 0.86974967 0.86461407
B-Blood_Pressure 108 17 27 0.864 0.8 0.83076924
I-O2_Saturation 99 23 36 0.8114754 0.73333335 0.77042806
B-Frequency 519 53 63 0.9073427 0.8917526 0.8994801
I-Triglycerides 3 0 5 1.0 0.375 0.54545456
I-Female_Reproductive_Status 0 0 3 0.0 0.0 0.0
I-Duration 529 71 112 0.88166666 0.82527304 0.8525383
I-Diabetes 41 8 1 0.8367347 0.97619045 0.90109897
B-Race_Ethnicity 77 0 4 1.0 0.9506173 0.9746836
I-Gender 0 0 2 0.0 0.0 0.0
I-Height 40 1 18 0.9756098 0.6896552 0.8080808
B-Communicable_Disease 11 2 8 0.84615386 0.57894737 0.68749994
I-Family_History_Header 35 0 1 1.0 0.9722222 0.9859155
B-LDL 3 1 0 0.75 1.0 0.85714287
B-Form 169 41 40 0.8047619 0.80861247 0.8066826
I-Race_Ethnicity 2 0 2 1.0 0.5 0.6666667
B-Psychological_Condition 114 12 19 0.9047619 0.85714287 0.88030887
I-Drug_BrandName 14 12 12 0.53846157 0.53846157 0.53846157
I-Hypertension 2 2 10 0.5 0.16666667 0.25
I-Age 196 43 7 0.8200837 0.9655172 0.88687783
B-EKG_Findings 38 18 35 0.6785714 0.5205479 0.58914727
B-Employment 193 31 41 0.86160713 0.8247863 0.8427947
I-Oncological 333 38 23 0.8975741 0.9353933 0.9160936
B-Time 320 34 23 0.9039548 0.9329446 0.91822094
B-Treatment 129 36 61 0.7818182 0.6789474 0.7267606
B-Temperature 104 15 19 0.8739496 0.8455285 0.85950416
B-Tumor_Finding 1 2 10 0.33333334 0.09090909 0.14285715
I-Procedure 2667 348 335 0.8845771 0.8884077 0.8864883
B-Relationship_Status 37 3 3 0.925 0.925 0.925
B-Pregnancy 77 17 15 0.81914896 0.8369565 0.827957
B-Fetus_NewBorn 18 7 18 0.72 0.5 0.59016395
I-Total_Cholesterol 14 1 5 0.93333334 0.7368421 0.8235294
I-Route 193 17 13 0.9190476 0.9368932 0.92788464
B-Birth_Entity 1 7 1 0.125 0.5 0.2
I-Communicable_Disease 5 1 2 0.8333333 0.71428573 0.7692307
I-Medical_History_Header 119 0 3 1.0 0.97540987 0.98755187
I-Imaging_Technique 10 1 15 0.90909094 0.4 0.5555555
B-Smoking 96 5 5 0.95049506 0.95049506 0.95049506
I-Labour_Delivery 29 20 9 0.59183675 0.7631579 0.6666667
I-Death_Entity 3 1 0 0.75 1.0 0.85714287
B-Diabetes 77 3 3 0.9625 0.9625 0.9625
B-HDL 2 0 1 1.0 0.6666667 0.8
B-Drug_BrandName 792 67 61 0.9220023 0.9284877 0.92523366
B-Gender 4498 58 63 0.9872695 0.9861872 0.9867281
B-Metastasis 5 2 8 0.71428573 0.3846154 0.5
I-Relationship_Status 0 0 4 0.0 0.0 0.0
B-Cancer_Modifier 4 0 1 1.0 0.8 0.88888896
B-Vaccine 39 6 7 0.8666667 0.84782606 0.8571428
I-Heart_Disease 317 47 47 0.8708791 0.8708791 0.8708791
I-Dosage 216 47 126 0.82129276 0.6315789 0.7140496
B-Staging 0 0 2 0.0 0.0 0.0
B-Social_History_Header 65 8 3 0.89041096 0.9558824 0.92198586
B-External_body_part_or_region 1792 195 229 0.9018621 0.8866898 0.8942116
I-Clinical_Dept 559 23 47 0.9604811 0.92244226 0.9410775
I-Tumor_Finding 0 13 11 0.0 0.0 0.0
I-Test 1919 311 305 0.8605381 0.8628597 0.8616973
I-Frequency 447 53 68 0.894 0.86796117 0.88078815
B-Age 461 50 22 0.90215266 0.9544513 0.9275654
B-Pulse 96 14 18 0.8727273 0.84210527 0.85714287
I-Symptom 3408 1152 1091 0.7473684 0.75750166 0.7524009
I-Form 1 5 2 0.16666667 0.33333334 0.22222224
I-Pregnancy 66 8 36 0.8918919 0.64705884 0.75000006
I-LDL 5 2 6 0.71428573 0.45454547 0.5555556
I-Diet 40 7 25 0.85106385 0.61538464 0.7142857
I-Blood_Pressure 165 35 36 0.825 0.8208955 0.8229426
I-ImagingFindings 118 60 72 0.66292137 0.6210526 0.6413044
I-Date 195 11 10 0.9466019 0.9512195 0.9489051
I-Hyperlipidemia 1 1 0 0.5 1.0 0.6666667
B-Route 755 69 83 0.91626215 0.90095466 0.90854394
B-Duration 219 30 72 0.8795181 0.7525773 0.81111115
B-Medical_History_Header 84 6 3 0.93333334 0.9655172 0.9491525
I-Metastasis 3 4 4 0.42857143 0.42857143 0.42857143
I-Allergen 0 1 3 0.0 0.0 0.0
B-Respiration 53 19 15 0.7361111 0.7794118 0.75714284
I-External_body_part_or_region 429 73 62 0.85458165 0.8737271 0.86404836
I-BMI 12 3 3 0.8 0.8 0.8000001
B-Internal_organ_or_component 4361 475 509 0.90177834 0.89548254 0.8986194
I-Weight 146 14 21 0.9125 0.8742515 0.8929664
B-Disease_Syndrome_Disorder 2222 283 350 0.88702595 0.86391914 0.8753201
B-Symptom 4910 711 744 0.87351006 0.8684117 0.87095344
B-VS_Finding 207 28 44 0.8808511 0.8247012 0.8518518
I-Disease_Syndrome_Disorder 1659 201 383 0.89193547 0.8124388 0.85033315
I-Modifier 162 67 138 0.70742357 0.54 0.61247635
I-Medical_Device 1786 245 176 0.8793698 0.9102956 0.89456546
B-Oncological 354 44 29 0.8894472 0.92428195 0.90653
I-Temperature 172 16 24 0.9148936 0.877551 0.8958333
I-Employment 108 18 32 0.85714287 0.7714286 0.81203
I-Psychological_Condition 40 9 7 0.81632656 0.85106385 0.8333334
B-Family_History_Header 47 0 4 1.0 0.92156863 0.9591837
I-Direction 186 42 23 0.81578946 0.8899522 0.8512586
I-HDL 3 0 2 1.0 0.6 0.75
Macro-average 71581 9121 10180 0.7729799 0.721845 0.7465378
Micro-average 71581 9121 10180 0.8869793 0.8754908 0.88119763