Description
Pretrained named entity recognition deep learning model for radiology related texts and reports.
Predicted Entities
ImagingTest, Imaging_Technique, ImagingFindings, OtherFindings, BodyPart, Direction, Test, Symptom, Disease_Syndrome_Disorder, Medical_Device, Procedure, Measurements, Units
Live Demo Open in Colab Copy S3 URI
How to use
Use as part of an NLP pipeline with the following stages: DocumentAssembler, SentenceDetector, Tokenizer, WordEmbeddingsModel, NerDLModel. Add the NerConverter to the end of the pipeline to convert entity tokens into full entity chunks.
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models") \
.setInputCols(["document"]) \
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
radiology_ner = NerDLModel.pretrained("ner_radiology", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("entities")
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, radiology_ner, ner_converter])
data = 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."]]).toDF("text")
results = nlpPipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.setInputCols("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val radiology_ner = NerDLModel().pretrained("ner_radiology", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("entities")
val nlpPipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, radiology_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 = nlpPipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.radiology").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
| | chunks | entities |
|----|-----------------------|---------------------------|
| 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 | anteromedial aspect | Direction |
| 7 | left | Direction |
| 8 | shoulder | BodyPart |
| 9 | mass | ImagingFindings |
| 10 | isoechoic echotexture | ImagingFindings |
| 11 | muscle | BodyPart |
| 12 | internal color flow | ImagingFindings |
| 13 | benign fibrous tissue | ImagingFindings |
| 14 | lipoma | Disease_Syndrome_Disorder |
Model Information
| Model Name: | ner_radiology |
| Type: | ner |
| Compatibility: | Spark NLP 2.7.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Dependencies: | embeddings_clinical |
Data Source
Trained on a custom dataset comprising of MIMIC-CXR and MT Radiology texts
Benchmarking
label tp fp fn total precision recall f1
OtherFindings 8.0 15.0 63.0 71.0 0.3478 0.1127 0.1702
Measurements 481.0 30.0 15.0 496.0 0.9413 0.9698 0.9553
Direction 650.0 137.0 94.0 744.0 0.8259 0.8737 0.8491
ImagingFindings 1345.0 355.0 324.0 1669.0 0.7912 0.8059 0.7985
BodyPart 1942.0 335.0 290.0 2232.0 0.8529 0.8701 0.8614
Medical_Device 236.0 75.0 64.0 300.0 0.7588 0.7867 0.7725
Test 222.0 41.0 48.0 270.0 0.8441 0.8222 0.833
Procedure 269.0 117.0 116.0 385.0 0.6969 0.6987 0.6978
ImagingTest 263.0 50.0 43.0 306.0 0.8403 0.8595 0.8498
Symptom 498.0 101.0 132.0 630.0 0.8314 0.7905 0.8104
Disease_Syndrome_... 1180.0 258.0 200.0 1380.0 0.8206 0.8551 0.8375
Units 269.0 10.0 2.0 271.0 0.9642 0.9926 0.9782
Imaging_Technique 140.0 38.0 25.0 165.0 0.7865 0.8485 0.8163
macro - - - - - - 0.7524
micro - - - - - - 0.8315