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
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.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 = MedicalNerModel.pretrained("ner_radiology", "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, word_embeddings, radiology_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 = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val radiology_ner = MedicalNerModel().pretrained("ner_radiology", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
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 |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Data Source
Trained on a custom dataset comprising of MIMIC-CXR and MT Radiology texts
Benchmarking
+--------------------+------+-----+-----+------+---------+------+------+
| entity| 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|
+------------------+
|0.7524248724038437|
+------------------+
+------------------+
| micro|
+------------------+
|0.8315240382681794|
+------------------+