Description
Extract clinical entities from Radiology reports using pretrained NER model.
Predicted Entities
Kidney_Disease, HDL, Diet, Test, Imaging_Technique, Triglycerides, Obesity, Duration, Weight, Social_History_Header, ImagingTest, Labour_Delivery, Disease_Syndrome_Disorder, Communicable_Disease, Overweight, Units, Smoking, Score, Substance_Quantity, Form, Race_Ethnicity, Modifier, Hyperlipidemia, ImagingFindings, Psychological_Condition, OtherFindings, Cerebrovascular_Disease, Date, Test_Result, VS_Finding, Employment, Death_Entity, Gender, Oncological, Heart_Disease, Medical_Device, Total_Cholesterol, ManualFix, Time, Route, Pulse, Admission_Discharge, RelativeDate, O2_Saturation, Frequency, RelativeTime, Hypertension, Alcohol, Allergen, Fetus_NewBorn, Birth_Entity, Age, Respiration, Medical_History_Header, Oxygen_Therapy, Section_Header, LDL, Treatment, Vital_Signs_Header, Direction, BMI, Pregnancy, Sexually_Active_or_Sexual_Orientation, Symptom, Clinical_Dept, Measurements, Height, Family_History_Header, Substance, Strength, Injury_or_Poisoning, Relationship_Status, Blood_Pressure, Drug, Temperature, EKG_Findings, Diabetes, BodyPart, Vaccine, Procedure, Dosage
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("jsl_rd_ner_wip_greedy_clinical", "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([["EXAMPLE_TEXT"]]).toDF("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("jsl_rd_ner_wip_greedy_clinical", "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 result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.jsl.wip.clinical.rd").predict("""Put your text here.""")
Model Information
| Model Name: | jsl_rd_ner_wip_greedy_clinical |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Benchmarking
entity tp fp fn total precision recall f1
VS_Finding 306.0 129.0 119.0 425.0 0.7034 0.72 0.7116
Direction 8717.0 678.0 616.0 9333.0 0.9278 0.934 0.9309
Respiration 224.0 28.0 18.0 242.0 0.8889 0.9256 0.9069
Cerebrovascular_Disease 149.0 57.0 64.0 213.0 0.7233 0.6995 0.7112
Family_History_Header 315.0 1.0 3.0 318.0 0.9968 0.9906 0.9937
Heart_Disease 1087.0 198.0 141.0 1228.0 0.8459 0.8852 0.8651
ImagingFindings 5568.0 1112.0 1627.0 7195.0 0.8335 0.7739 0.8026
RelativeTime 422.0 138.0 100.0 522.0 0.7536 0.8084 0.78
Strength 96.0 51.0 54.0 150.0 0.6531 0.64 0.6465
BodyPart 20155.0 1698.0 1860.0 22015.0 0.9223 0.9155 0.9189
Smoking 151.0 16.0 5.0 156.0 0.9042 0.9679 0.935
Medical_Device 8162.0 885.0 821.0 8983.0 0.9022 0.9086 0.9054
EKG_Findings 131.0 37.0 83.0 214.0 0.7798 0.6121 0.6859
Pulse 382.0 44.0 50.0 432.0 0.8967 0.8843 0.8904
Psychological_Condition 195.0 32.0 43.0 238.0 0.859 0.8193 0.8387
Triglycerides 18.0 0.0 0.0 18.0 1.0 1.0 1.0
Overweight 6.0 2.0 1.0 7.0 0.75 0.8571 0.8
Obesity 68.0 3.0 5.0 73.0 0.9577 0.9315 0.9444
Admission_Discharge 376.0 26.0 24.0 400.0 0.9353 0.94 0.9377
HDL 11.0 0.0 5.0 16.0 1.0 0.6875 0.8148
Diabetes 227.0 9.0 12.0 239.0 0.9619 0.9498 0.9558
Section_Header 13630.0 476.0 413.0 14043.0 0.9663 0.9706 0.9684
Age 1174.0 129.0 94.0 1268.0 0.901 0.9259 0.9133
O2_Saturation 122.0 34.0 29.0 151.0 0.7821 0.8079 0.7948
Drug 9391.0 1505.0 928.0 10319.0 0.8619 0.9101 0.8853
Kidney_Disease 296.0 28.0 53.0 349.0 0.9136 0.8481 0.8796
Test 3980.0 721.0 925.0 4905.0 0.8466 0.8114 0.8286
Communicable_Disease 40.0 18.0 12.0 52.0 0.6897 0.7692 0.7273
Hypertension 163.0 16.0 10.0 173.0 0.9106 0.9422 0.9261
Oxygen_Therapy 123.0 36.0 27.0 150.0 0.7736 0.82 0.7961
Test_Result 1607.0 374.0 458.0 2065.0 0.8112 0.7782 0.7944
Modifier 1229.0 435.0 593.0 1822.0 0.7386 0.6745 0.7051
BMI 21.0 4.0 7.0 28.0 0.84 0.75 0.7925
Labour_Delivery 117.0 38.0 62.0 179.0 0.7548 0.6536 0.7006
Employment 414.0 65.0 93.0 507.0 0.8643 0.8166 0.8398
Fetus_NewBorn 118.0 68.0 87.0 205.0 0.6344 0.5756 0.6036
Clinical_Dept 1937.0 189.0 133.0 2070.0 0.9111 0.9357 0.9233
Time 637.0 43.0 27.0 664.0 0.9368 0.9593 0.9479
Procedure 7578.0 953.0 1088.0 8666.0 0.8883 0.8745 0.8813
ImagingTest 1712.0 213.0 281.0 1993.0 0.8894 0.859 0.8739
Diet 79.0 44.0 82.0 161.0 0.6423 0.4907 0.5563
Oncological 1088.0 188.0 103.0 1191.0 0.8527 0.9135 0.882
LDL 20.0 7.0 1.0 21.0 0.7407 0.9524 0.8333
Symptom 15940.0 3662.0 3035.0 18975.0 0.8132 0.8401 0.8264
Temperature 240.0 28.0 25.0 265.0 0.8955 0.9057 0.9006
Vital_Signs_Header 850.0 34.0 52.0 902.0 0.9615 0.9424 0.9518
Total_Cholesterol 43.0 6.0 7.0 50.0 0.8776 0.86 0.8687
Relationship_Status 51.0 3.0 9.0 60.0 0.9444 0.85 0.8947
Blood_Pressure 353.0 18.0 117.0 470.0 0.9515 0.7511 0.8395
Injury_or_Poisoning 1003.0 311.0 241.0 1244.0 0.7633 0.8063 0.7842
Treatment 335.0 98.0 91.0 426.0 0.7737 0.7864 0.78
Pregnancy 214.0 99.0 86.0 300.0 0.6837 0.7133 0.6982
Vaccine 29.0 3.0 10.0 39.0 0.9063 0.7436 0.8169
Height 105.0 10.0 45.0 150.0 0.913 0.7 0.7925
Disease_Syndrome_Disorder 8466.0 1568.0 1533.0 9999.0 0.8437 0.8467 0.8452
Frequency 1263.0 237.0 173.0 1436.0 0.842 0.8795 0.8604
Route 219.0 35.0 144.0 363.0 0.8622 0.6033 0.7099
Duration 978.0 199.0 338.0 1316.0 0.8309 0.7432 0.7846
Death_Entity 35.0 17.0 16.0 51.0 0.6731 0.6863 0.6796
Alcohol 102.0 24.0 21.0 123.0 0.8095 0.8293 0.8193
Date 840.0 43.0 13.0 853.0 0.9513 0.9848 0.9677
Hyperlipidemia 44.0 4.0 1.0 45.0 0.9167 0.9778 0.9462
Social_History_Header 284.0 6.0 27.0 311.0 0.9793 0.9132 0.9451
ManualFix 50.0 2.0 7.0 57.0 0.9615 0.8772 0.9174
Imaging_Technique 845.0 240.0 98.0 943.0 0.7788 0.8961 0.8333
Race_Ethnicity 141.0 0.0 5.0 146.0 1.0 0.9658 0.9826
RelativeDate 1691.0 394.0 194.0 1885.0 0.811 0.8971 0.8519
Gender 6800.0 105.0 130.0 6930.0 0.9848 0.9812 0.983
Dosage 122.0 67.0 81.0 203.0 0.6455 0.601 0.6224
Medical_History_Header 486.0 10.0 19.0 505.0 0.9798 0.9624 0.971
Sexually_Active_or_Sexual_Orientation 12.0 0.0 5.0 17.0 1.0 0.7059 0.8276
Substance 102.0 11.0 22.0 124.0 0.9027 0.8226 0.8608
Weight 346.0 26.0 65.0 411.0 0.9301 0.8418 0.8838
macro - - - - - - 0.8038
micro - - - - - - 0.8793