Description
Pretrained named entity recognition deep learning model for extracting covid-related clinical terminology from covid trials.
Predicted Entities
Stage, Severity, Virus, Trial_Design, Trial_Phase, N_Patients, Institution, Statistical_Indicator, Section_Header, Cell_Type, Cellular_component, Viral_components, Physiological_reaction, Biological_molecules, Admission_Discharge, Age, BMI, Cerebrovascular_Disease, Date, Death_Entity, Diabetes, Disease_Syndrome_Disorder, Dosage, Drug_Ingredient, Employment, Frequency, Gender, Heart_Disease, Hypertension, Obesity, Pulse, Race_Ethnicity, Respiration, Route, Smoking, Time, Total_Cholesterol, Treatment, VS_Finding, Vaccine
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
embeddings_clinical = WordEmbeddingsModel.pretrained('embeddings_clinical', 'en', 'clinical/models') \
.setInputCols(['sentence', 'token']) \
.setOutputCol('embeddings')
clinical_ner = MedicalNerModel.pretrained("ner_covid_trials", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings_clinical, clinical_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tract such as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR )."]], ["text"]))
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_covid_trials", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings_clinical, ner, ner_converter))
val data = Seq("""In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tract such as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.covid_trials").predict("""In December 2019 , a group of patients with the acute respiratory disease was detected in Wuhan , Hubei Province of China . A month later , a new beta-coronavirus was identified as the cause of the 2019 coronavirus infection . SARS-CoV-2 is a coronavirus that belongs to the group of β-coronaviruses of the subgenus Coronaviridae . The SARS-CoV-2 is the third known zoonotic coronavirus disease after severe acute respiratory syndrome ( SARS ) and Middle Eastern respiratory syndrome ( MERS ). The diagnosis of SARS-CoV-2 recommended by the WHO , CDC is the collection of a sample from the upper respiratory tract ( nasal and oropharyngeal exudate ) or from the lower respiratory tract such as expectoration of endotracheal aspirate and bronchioloalveolar lavage and its analysis using the test of real-time polymerase chain reaction ( qRT-PCR ).""")
Results
| | chunk | begin | end | entity |
|---:|:------------------------------------|--------:|------:|:--------------------------|
| 0 | December 2019 | 3 | 15 | Date |
| 1 | acute respiratory disease | 48 | 72 | Disease_Syndrome_Disorder |
| 2 | beta-coronavirus | 146 | 161 | Virus |
| 3 | 2019 coronavirus infection | 198 | 223 | Disease_Syndrome_Disorder |
| 4 | SARS-CoV-2 | 227 | 236 | Virus |
| 5 | coronavirus | 243 | 253 | Virus |
| 6 | β-coronaviruses | 284 | 298 | Virus |
| 7 | subgenus Coronaviridae | 307 | 328 | Virus |
| 8 | SARS-CoV-2 | 336 | 345 | Virus |
| 9 | zoonotic coronavirus disease | 366 | 393 | Disease_Syndrome_Disorder |
| 10 | severe acute respiratory syndrome | 401 | 433 | Disease_Syndrome_Disorder |
| 11 | SARS | 437 | 440 | Disease_Syndrome_Disorder |
| 12 | Middle Eastern respiratory syndrome | 448 | 482 | Disease_Syndrome_Disorder |
| 13 | MERS | 486 | 489 | Disease_Syndrome_Disorder |
| 14 | SARS-CoV-2 | 511 | 520 | Virus |
| 15 | WHO | 541 | 543 | Institution |
| 16 | CDC | 547 | 549 | Institution |
Model Information
| Model Name: | ner_covid_trials |
| Compatibility: | Healthcare NLP 3.3.2 |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
This model is trained on data sampled from clinicaltrials.gov - covid trials, and annotated in-house.
Benchmarking
label tp fp fn prec rec f1
B-Cerebrovascular_Disease 11 1 1 0.9166667 0.9166667 0.9166667
I-Cerebrovascular_Disease 2 0 0 1.0 1.0 1.0
I-Vaccine 20 2 7 0.90909094 0.7407407 0.81632656
I-N_Patients 2 2 5 0.5 0.2857143 0.36363637
B-Heart_Disease 32 8 11 0.8 0.74418604 0.7710843
I-Institution 35 15 56 0.7 0.3846154 0.49645394
B-Obesity 8 0 0 1.0 1.0 1.0
I-Trial_Phase 16 9 4 0.64 0.8 0.7111111
B-Dosage 50 28 43 0.64102566 0.53763443 0.5847953
B-Hypertension 10 0 0 1.0 1.0 1.0
I-Stage 0 1 2 0.0 0.0 0.0
I-Cell_Type 82 31 17 0.7256637 0.82828283 0.7735849
B-Admission_Discharge 95 1 4 0.9895833 0.959596 0.974359
B-Date 88 10 9 0.8979592 0.9072165 0.9025641
I-Admission_Discharge 0 0 2 0.0 0.0 0.0
I-Drug_Ingredient 104 71 57 0.5942857 0.6459627 0.61904764
B-Stage 0 2 6 0.0 0.0 0.0
B-Cellular_component 18 22 28 0.45 0.39130434 0.41860464
B-Total_Cholesterol 5 0 0 1.0 1.0 1.0
I-Biological_molecules 52 39 87 0.5714286 0.37410071 0.45217392
I-Virus 56 26 24 0.68292683 0.7 0.69135803
B-BMI 9 2 2 0.8181818 0.8181818 0.8181818
B-Drug_Ingredient 330 82 84 0.80097085 0.79710144 0.7990315
B-Severity 43 22 16 0.6615385 0.7288136 0.69354844
B-Section_Header 86 27 28 0.76106197 0.75438595 0.7577093
I-Treatment 35 7 32 0.8333333 0.52238804 0.64220184
I-Pulse 1 1 0 0.5 1.0 0.6666667
I-Respiration 1 2 0 0.33333334 1.0 0.5
I-Section_Header 95 30 67 0.76 0.58641976 0.66202086
I-VS_Finding 2 2 1 0.5 0.6666667 0.57142854
B-Death_Entity 14 3 6 0.8235294 0.7 0.7567568
B-Statistical_Indicator 79 53 52 0.5984849 0.60305345 0.60076046
B-Frequency 31 8 8 0.7948718 0.7948718 0.79487187
I-Diabetes 3 0 0 1.0 1.0 1.0
B-Race_Ethnicity 3 0 0 1.0 1.0 1.0
B-Cell_Type 90 43 35 0.6766917 0.72 0.6976744
B-N_Patients 37 11 9 0.7708333 0.8043478 0.787234
B-Trial_Phase 13 4 2 0.7647059 0.8666667 0.8125
B-Biological_molecules 171 68 122 0.71548116 0.58361775 0.6428571
I-Hypertension 1 0 0 1.0 1.0 1.0
I-Age 15 0 3 1.0 0.8333333 0.90909094
B-Employment 75 28 21 0.7281553 0.78125 0.7537688
B-Time 8 0 2 1.0 0.8 0.88888896
I-Physiological_reaction 11 11 36 0.5 0.23404256 0.3188406
I-Viral_components 22 2 21 0.9166667 0.5116279 0.6567164
B-Treatment 65 8 19 0.89041096 0.77380955 0.8280255
B-Trial_Design 26 22 19 0.5416667 0.5777778 0.5591398
I-Severity 6 13 4 0.31578946 0.6 0.41379312
I-Route 1 1 1 0.5 0.5 0.5
B-Smoking 3 1 0 0.75 1.0 0.85714287
B-Diabetes 10 0 0 1.0 1.0 1.0
B-Gender 21 2 6 0.9130435 0.7777778 0.84
I-Trial_Design 41 25 13 0.6212121 0.7592593 0.6833333
B-Virus 105 28 34 0.7894737 0.7553957 0.77205884
B-Vaccine 28 9 2 0.7567568 0.93333334 0.8358209
I-Heart_Disease 32 6 15 0.84210527 0.68085104 0.75294113
I-Dosage 41 32 35 0.56164384 0.5394737 0.5503355
I-Cellular_component 12 7 19 0.6315789 0.38709676 0.48
I-Frequency 27 9 8 0.75 0.7714286 0.7605634
B-Age 14 10 11 0.5833333 0.56 0.57142854
B-Pulse 1 1 0 0.5 1.0 0.6666667
I-Statistical_Indicator 40 27 44 0.5970149 0.47619048 0.52980137
I-Date 56 0 7 1.0 0.8888889 0.94117653
B-Route 38 7 12 0.84444445 0.76 0.79999995
B-Institution 24 14 44 0.6315789 0.3529412 0.4528302
B-Viral_components 19 7 19 0.7307692 0.5 0.59375
B-Respiration 1 2 0 0.33333334 1.0 0.5
I-BMI 10 1 4 0.90909094 0.71428573 0.8000001
B-Disease_Syndrome_Disorder 656 76 85 0.89617485 0.88529015 0.8906992
B-VS_Finding 23 5 1 0.8214286 0.9583333 0.8846154
B-Physiological_reaction 9 4 26 0.6923077 0.25714287 0.37500003
I-Disease_Syndrome_Disorder 336 50 132 0.8704663 0.71794873 0.78688526
I-Employment 23 9 12 0.71875 0.6571429 0.6865672
Macro-average 3529 1050 1482 0.7160117 0.700098 0.70796543
Micro-average 3529 1050 1482 0.7706923 0.7042506 0.73597497