Description
Extract clinical entities from Radiology reports using pretrained NER model.
Predicted Entities
Test_Result, OtherFindings, BodyPart, ImagingFindings, Disease_Syndrome_Disorder, ImagingTest, Measurements, Procedure, Score, Test, Medical_Device, Direction, Symptom, Imaging_Technique, ManualFix, Units
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 = BertEmbeddings.pretrained('biobert_pubmed_base_cased') \
.setInputCols(['sentence', 'token']) \
.setOutputCol('embeddings')
clinical_ner = MedicalNerModel.pretrained("jsl_rd_ner_wip_greedy_biobert", "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([["Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma."]], ["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 = BertEmbeddings.pretrained("biobert_pubmed_base_cased")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("jsl_rd_ner_wip_greedy_biobert", "en", "clinical/models")
.setInputCols("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("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.radiology.wip_greedy_biobert").predict("""Bilateral breast ultrasound was subsequently performed, which demonstrated an ovoid mass measuring approximately 0.5 x 0.5 x 0.4 cm in diameter located within the anteromedial aspect of the left shoulder. This mass demonstrates isoechoic echotexture to the adjacent muscle, with no evidence of internal color flow. This may represent benign fibrous tissue or a lipoma.""")
Results
| | chunk | entity |
|---:|:----------------------|:--------------------------|
| 0 | Bilateral | Direction |
| 1 | breast | BodyPart |
| 2 | ultrasound | ImagingTest |
| 3 | ovoid mass | ImagingFindings |
| 4 | 0.5 x 0.5 x 0.4 | Measurements |
| 5 | cm | Units |
| 6 | left | Direction |
| 7 | shoulder | BodyPart |
| 8 | mass | ImagingFindings |
| 9 | isoechoic echotexture | ImagingFindings |
| 10 | muscle | BodyPart |
| 11 | internal color flow | ImagingFindings |
| 12 | benign fibrous tissue | ImagingFindings |
| 13 | lipoma | Disease_Syndrome_Disorder |
Model Information
| Model Name: | jsl_rd_ner_wip_greedy_biobert |
| Compatibility: | Healthcare NLP 3.1.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
Trained on Dataset annotated by John Snow Labs
Benchmarking
label tp fp fn prec rec f1
B-Units 253 7 11 0.9730769 0.9583333 0.9656488
B-Medical_Device 382 109 74 0.7780040 0.8377193 0.8067581
B-BodyPart 2645 347 276 0.8840241 0.9055118 0.8946389
I-BodyPart 645 142 135 0.819568 0.8269231 0.8232291
B-Imaging_Technique 137 36 33 0.7919075 0.8058823 0.7988338
B-Procedure 260 93 130 0.7365439 0.6666667 0.6998653
B-Direction 1573 136 123 0.9204213 0.9274764 0.9239353
I-ImagingTest 30 9 32 0.7692308 0.4838709 0.5940594
I-Test_Result 2 0 0 1 1 1
B-Measurements 452 24 30 0.9495798 0.9377593 0.9436326
B-ImagingFindings 1929 679 542 0.7396472 0.7806556 0.7595984
B-Test 146 17 49 0.8957055 0.7487179 0.8156425
B-ManualFix 2 0 2 1 0.5 0.6666667
I-Procedure 147 91 106 0.6176470 0.5810277 0.598778
I-Imaging_Technique 75 63 26 0.5434782 0.7425743 0.6276151
I-Measurements 45 3 6 0.9375 0.8823529 0.9090909
B-ImagingTest 328 36 85 0.9010989 0.7941888 0.8442728
I-Test 26 9 34 0.7428571 0.4333333 0.5473684
I-Symptom 138 62 142 0.69 0.4928571 0.575
I-ImagingFindings 1348 617 662 0.6860051 0.6706468 0.678239
B-Disease_Syndrome_Disorder 1068 298 243 0.7818448 0.8146453 0.7979080
B-Symptom 523 110 190 0.8262243 0.7335203 0.7771174
I-Disease_Syndrome_Disorder 377 168 171 0.6917431 0.6879562 0.6898445
I-Medical_Device 369 72 62 0.8367347 0.8561485 0.8463302
I-Direction 352 38 41 0.9025641 0.8956743 0.899106
Macro-average 13272 3200 3313 0.7195612 0.6489194 0.6824170
Micro-average 13272 3200 3313 0.8057309 0.8002412 0.8029767