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 trained using BERT token embeddings biobert_pubmed_base_cased.
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
Strength, Pregnancy_Delivery_Puerperium, Female_Reproductive_Status, Fetus_NewBorn, Age, Alcohol, Treatment, Internal_organ_or_component, Vital_Signs_Header, Dosage, Employment, Gender, Disease_Syndrome_Disorder, Pregnancy, Symptom, Clinical_Dept, Medical_Device, Temperature, Hypertension, Cerebrovascular_Disease, Psychological_Condition, Respiration, Direction, Metastasis, Injury_or_Poisoning, Birth_Entity, Allergen, Labour_Delivery, Overweight, Family_History_Header, Section_Header, Diabetes, Hyperlipidemia, Death_Entity, Route, Duration, Admission_Discharge, Total_Cholesterol, Performance_Status, LDL, RelativeDate, Test_Result, Height, Procedure, Date, Cancer_Modifier, BMI, External_body_part_or_region, Kidney_Disease, Modifier, Oncology_Therapy, Drug_BrandName, Form, Substance, Social_History_Header, Obesity, Oncological, Sexually_Active_or_Sexual_Orientation, EKG_Findings, Oxygen_Therapy, Frequency, Relationship_Status, Communicable_Disease, Imaging_Technique, Vaccine, Pulse, Tumor_Finding, Heart_Disease, Time, ImagingFindings, HDL, O2_Saturation, Weight, Medical_History_Header, Blood_Pressure, Puerperium, Smoking, Substance_Quantity, RelativeTime, Test, Race_Ethnicity, Diet, Staging, Triglycerides, Drug_Ingredient, VS_Finding
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 = BertEmbeddings.pretrained("biobert_pubmed_base_cased")\
.setInputCols(["sentence", "token"]) \
.setOutputCol("embeddings")
jsl_ner = MedicalNerModel.pretrained("ner_jsl_biobert", "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 = BertEmbeddings.pretrained("biobert_pubmed_base_cased")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val jsl_ner = MedicalNerModel.pretrained("ner_jsl_biobert", "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.biobert").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 | entity |
|---:|:------------------------------------------|:-----------------------------|
| 0 | 21-day-old | Age |
| 1 | Caucasian | Race_Ethnicity |
| 2 | male | Gender |
| 3 | for 2 days | Duration |
| 4 | congestion | Symptom |
| 5 | mom | Gender |
| 6 | suctioning yellow discharge | Symptom |
| 7 | nares | External_body_part_or_region |
| 8 | she | Gender |
| 9 | mild | Modifier |
| 10 | problems with his breathing while feeding | Symptom |
| 11 | perioral cyanosis | Symptom |
| 12 | retractions | Symptom |
| 13 | One day ago | RelativeDate |
| 14 | mom | Gender |
| 15 | tactile temperature | Symptom |
| 16 | Tylenol | Drug_BrandName |
| 17 | decreased p.o | Symptom |
| 18 | His | Gender |
| 19 | from 20 minutes q.2h. to 5 to 10 minutes | Frequency |
| 20 | his | Gender |
| 21 | respiratory congestion | Symptom |
| 22 | He | Gender |
| 23 | tired | Symptom |
| 24 | fussy | Symptom |
| 25 | over the past | RelativeDate |
Model Information
| Model Name: | ner_jsl_biobert |
| Compatibility: | Healthcare NLP 3.2.0+ |
| 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-Oxygen_Therapy 114 41 38 0.7354839 0.75 0.742671
B-Cerebrovascular_Disease 42 16 19 0.7241379 0.6885246 0.7058824
B-Triglycerides 2 0 2 1 0.5 0.6666667
I-Cerebrovascular_Disease 17 11 17 0.60714287 0.5 0.54838705
B-Medical_Device 2568 334 400 0.88490695 0.8652291 0.8749574
B-Labour_Delivery 31 8 17 0.7948718 0.6458333 0.71264374
I-Vaccine 12 3 4 0.8 0.75 0.7741936
I-Obesity 4 2 2 0.6666667 0.6666667 0.6666667
B-RelativeTime 126 71 94 0.6395939 0.57272726 0.60431653
B-Heart_Disease 254 80 43 0.76047903 0.8552188 0.8050713
B-Procedure 2019 270 302 0.88204455 0.86988366 0.8759219
I-RelativeTime 183 93 44 0.6630435 0.8061674 0.72763425
B-Obesity 46 5 5 0.9019608 0.9019608 0.9019608
I-RelativeDate 629 125 76 0.8342175 0.89219856 0.8622344
B-O2_Saturation 51 28 28 0.6455696 0.6455696 0.6455696
B-Direction 3016 219 360 0.93230295 0.8933649 0.9124187
I-Alcohol 3 2 3 0.6 0.5 0.54545456
I-Oxygen_Therapy 91 67 28 0.5759494 0.7647059 0.6570397
B-Dosage 277 82 86 0.7715877 0.7630854 0.767313
B-Injury_or_Poisoning 336 56 86 0.85714287 0.79620856 0.8255528
B-Hypertension 104 9 2 0.920354 0.9811321 0.9497717
I-Test_Result 1173 101 119 0.9207221 0.90789473 0.9142634
B-Substance_Quantity 4 8 0 0.33333334 1 0.5
B-Alcohol 68 9 6 0.8831169 0.9189189 0.90066224
B-Height 19 10 11 0.6551724 0.6333333 0.64406776
I-Substance 10 2 6 0.8333333 0.625 0.71428573
B-RelativeDate 416 91 58 0.82051283 0.87763715 0.84811425
B-Admission_Discharge 245 12 7 0.9533074 0.9722222 0.9626719
B-Date 316 17 14 0.9489489 0.95757574 0.9532428
B-Kidney_Disease 68 13 23 0.83950615 0.74725276 0.7906977
I-Strength 505 50 46 0.9099099 0.9165154 0.91320074
I-Injury_or_Poisoning 255 73 132 0.777439 0.65891474 0.71328676
I-Drug_Ingredient 279 102 38 0.7322835 0.8801262 0.799427
I-Time 323 31 17 0.9124294 0.95 0.9308358
B-Substance 46 6 12 0.88461536 0.79310346 0.8363636
B-Total_Cholesterol 8 4 7 0.6666667 0.53333336 0.59259266
I-Vital_Signs_Header 152 18 2 0.89411765 0.987013 0.9382716
I-Internal_organ_or_component 2755 490 350 0.8489985 0.88727856 0.8677165
B-Hyperlipidemia 37 7 3 0.84090906 0.925 0.8809524
I-Sexually_Active_or_Sexual_Orientation 5 0 0 1 1 1
B-Sexually_Active_or_Sexual_Orientation 5 0 2 1 0.71428573 0.8333334
I-Fetus_NewBorn 44 60 28 0.42307693 0.6111111 0.5
B-BMI 4 1 2 0.8 0.6666667 0.72727275
B-ImagingFindings 71 40 83 0.6396396 0.46103895 0.53584903
B-Drug_Ingredient 1636 235 222 0.8743987 0.8805167 0.877447
B-Test_Result 1369 180 188 0.883796 0.879255 0.8815196
B-Section_Header 2735 115 116 0.95964915 0.9593125 0.95948076
I-Treatment 84 28 35 0.75 0.7058824 0.7272728
B-Clinical_Dept 721 101 89 0.87712896 0.8901235 0.8835784
I-Kidney_Disease 106 9 7 0.9217391 0.9380531 0.9298245
I-Pulse 140 49 35 0.7407407 0.8 0.7692308
B-Test 2267 375 390 0.8580621 0.8532179 0.85563314
B-Weight 70 16 16 0.81395346 0.81395346 0.81395346
I-Respiration 61 5 28 0.92424244 0.6853933 0.78709674
I-EKG_Findings 50 38 44 0.5681818 0.5319149 0.5494506
I-Section_Header 1998 108 65 0.94871795 0.9684925 0.95850325
I-VS_Finding 36 31 29 0.53731346 0.5538462 0.5454546
B-Strength 541 51 54 0.9138514 0.9092437 0.9115417
I-Social_History_Header 43 3 5 0.9347826 0.8958333 0.9148936
B-Vital_Signs_Header 228 26 3 0.8976378 0.987013 0.94020617
B-Death_Entity 30 5 4 0.85714287 0.88235295 0.86956525
B-Modifier 2023 367 375 0.84644353 0.8436197 0.8450293
B-Blood_Pressure 110 23 32 0.8270677 0.7746479 0.8
I-O2_Saturation 93 56 29 0.62416106 0.76229507 0.6863469
B-Frequency 564 53 61 0.91410047 0.9024 0.9082126
I-Triglycerides 2 0 1 1 0.6666667 0.8
I-Duration 510 71 88 0.8777969 0.8528428 0.86513996
I-Diabetes 35 2 5 0.9459459 0.875 0.9090909
B-Race_Ethnicity 67 2 4 0.9710145 0.943662 0.9571429
I-Height 72 23 9 0.75789475 0.8888889 0.8181819
B-Communicable_Disease 12 5 8 0.7058824 0.6 0.6486487
I-Family_History_Header 57 3 1 0.95 0.98275864 0.9661017
B-LDL 1 0 2 1 0.33333334 0.5
B-Form 180 38 31 0.82568806 0.8530806 0.8391608
I-Race_Ethnicity 2 1 0 0.6666667 1 0.8
B-Psychological_Condition 87 15 20 0.85294116 0.8130841 0.83253586
I-Drug_BrandName 25 8 18 0.75757575 0.5813953 0.6578947
I-Age 182 18 33 0.91 0.8465116 0.87710845
B-EKG_Findings 41 19 24 0.68333334 0.63076925 0.65599996
B-Employment 161 16 45 0.90960455 0.7815534 0.8407311
I-Oncological 338 32 62 0.91351354 0.845 0.8779221
B-Time 335 42 19 0.88859415 0.9463277 0.91655266
B-Treatment 98 43 63 0.69503546 0.6086956 0.6490066
B-Temperature 97 13 20 0.8818182 0.82905984 0.8546256
I-Procedure 2657 326 438 0.89071405 0.8584814 0.8743007
B-Relationship_Status 34 4 3 0.8947368 0.9189189 0.90666664
B-Pregnancy 51 25 21 0.67105263 0.7083333 0.68918914
B-Fetus_NewBorn 30 31 27 0.4918033 0.5263158 0.5084746
I-Total_Cholesterol 10 2 8 0.8333333 0.5555556 0.66666675
I-Route 205 16 21 0.9276018 0.90707964 0.91722596
I-Communicable_Disease 6 4 2 0.6 0.75 0.6666667
I-Medical_History_Header 116 5 10 0.9586777 0.9206349 0.9392713
B-Smoking 85 4 3 0.9550562 0.96590906 0.960452
I-Labour_Delivery 30 5 22 0.85714287 0.5769231 0.6896552
I-Death_Entity 4 1 1 0.8 0.8 0.8000001
B-Diabetes 87 5 5 0.9456522 0.9456522 0.9456522
B-HDL 1 1 0 0.5 1 0.6666667
B-Drug_BrandName 828 112 96 0.8808511 0.8961039 0.88841206
B-Gender 4420 61 62 0.98638695 0.9861669 0.98627687
B-Vaccine 13 0 8 1 0.61904764 0.7647059
I-Heart_Disease 315 145 27 0.6847826 0.92105263 0.7855362
I-Dosage 214 75 64 0.7404844 0.76978415 0.7548501
B-Social_History_Header 72 3 6 0.96 0.9230769 0.9411765
B-External_body_part_or_region 1759 194 376 0.90066564 0.8238876 0.8605675
I-Clinical_Dept 531 43 52 0.9250871 0.9108062 0.9178911
I-Test 1692 404 352 0.80725193 0.82778865 0.81739134
I-Frequency 445 66 61 0.8708415 0.8794466 0.87512296
B-Age 492 28 39 0.9461538 0.92655367 0.9362512
B-Pulse 86 31 30 0.73504275 0.7413793 0.7381974
I-Symptom 3072 1404 1050 0.6863271 0.7452693 0.71458477
I-Pregnancy 43 25 26 0.63235295 0.6231884 0.6277372
I-LDL 3 0 1 1 0.75 0.85714287
I-Diet 29 15 26 0.65909094 0.5272727 0.5858585
I-Blood_Pressure 171 52 35 0.76681614 0.8300971 0.79720277
I-ImagingFindings 153 86 88 0.64016736 0.6348548 0.6375
I-Date 184 10 9 0.9484536 0.9533679 0.9509044
B-Route 726 77 80 0.9041096 0.90074444 0.9024239
B-Duration 212 29 50 0.87966806 0.8091603 0.84294236
B-Medical_History_Header 89 8 5 0.91752577 0.9468085 0.9319371
I-Metastasis 5 0 1 1 0.8333333 0.90909094
B-Respiration 49 10 18 0.8305085 0.73134327 0.77777773
I-External_body_part_or_region 431 49 133 0.8979167 0.7641844 0.82567054
I-BMI 13 2 3 0.8666667 0.8125 0.83870965
B-Internal_organ_or_component 4260 612 634 0.8743842 0.8704536 0.8724145
I-Weight 177 42 16 0.8082192 0.91709846 0.8592233
B-Disease_Syndrome_Disorder 2091 367 318 0.8506916 0.867995 0.85925627
B-Symptom 4752 913 803 0.83883494 0.85544556 0.84705883
B-VS_Finding 180 46 45 0.79646015 0.8 0.7982262
I-Disease_Syndrome_Disorder 1592 331 309 0.8278731 0.83745396 0.832636
I-Modifier 148 96 128 0.60655737 0.5362319 0.56923074
I-Medical_Device 1677 235 266 0.87709206 0.8630983 0.870039
B-Oncological 381 33 44 0.9202899 0.8964706 0.90822405
I-Temperature 154 12 34 0.92771083 0.81914896 0.8700565
I-Employment 82 19 30 0.8118812 0.73214287 0.76995313
I-Psychological_Condition 25 2 7 0.9259259 0.78125 0.8474576
B-Family_History_Header 58 2 2 0.96666664 0.96666664 0.96666664
I-Direction 189 29 49 0.8669725 0.7941176 0.8289474
I-HDL 1 2 0 0.33333334 1 0.5
Macro-average 69137 11083 11027 0.7179756 0.7057431 0.7118068
Micro-average 69137 11083 11027 0.8618424 0.8624444 0.86214334