Description
Assign assertion status to clinical entities extracted by Radiology NER based on their context in the text.
Predicted Entities
Confirmed, Suspected, Negative.
Live Demo Open in Colab Download
How to use
Extract radiology entities using the radiology NER model in the pipeline and assign assertion status for them with assertion_dl_radiology pretrained model.
...
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("ner_chunk")\
.setWhiteList(["ImagingFindings"])
radiology_assertion = AssertionDLModel.pretrained("assertion_dl_radiology", "en", "clinical/models") \
.setInputCols(["sentence", "ner_chunk", "embeddings"]) \
.setOutputCol("assertion")
nlpPipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, word_embeddings, radiology_ner, ner_converter, radiology_assertion])
empty_data = spark.createDataFrame([[""]]).toDF("text")
model = nlpPipeline.fit(empty_data)
text = """Blunting of the left costophrenic angle on the lateral view posteriorly suggests a small left pleural effusion. No right-sided pleural effusion or pneumothorax is definitively seen. There are mildly displaced fractures of the left lateral 8th and likely 9th ribs."""
result = model.transform(spark.createDataFrame([[text]]).toDF("text"))
...
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 = NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("ImagingFindings"))
val radiology_assertion = AssertionDLModel.pretrained("assertion_dl_radiology", "en", "clinical/models")
.setInputCols(Array("sentence", "ner_chunk", "embeddings"))
.setOutputCol("assertion")
val nlpPipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, word_embeddings, radiology_ner, ner_converter, radiology_assertion))
val result = pipeline.fit(Seq.empty["Blunting of the left costophrenic angle on the lateral view posteriorly suggests a small left pleural effusion. No right-sided pleural effusion or pneumothorax is definitively seen. There are mildly displaced fractures of the left lateral 8th and likely 9th ribs."].toDS.toDF("text")).transform(data)
Results
+---------------------------------------------------------------------------------------------------------------+-------------------+---------------+-------+---------+
|sentences |chunk |ner_label |sent_id|assertion|
+---------------------------------------------------------------------------------------------------------------+-------------------+---------------+-------+---------+
|Blunting of the left costophrenic angle on the lateral view posteriorly suggests a small left pleural effusion.|Blunting |ImagingFindings|0 |Confirmed|
|Blunting of the left costophrenic angle on the lateral view posteriorly suggests a small left pleural effusion.|effusion |ImagingFindings|0 |Suspected|
|No right-sided pleural effusion or pneumothorax is definitively seen. |effusion |ImagingFindings|1 |Negative |
|No right-sided pleural effusion or pneumothorax is definitively seen. |pneumothorax |ImagingFindings|1 |Negative |
|There are mildly displaced fractures of the left lateral 8th and likely 9th ribs. |displaced fractures|ImagingFindings|2 |Confirmed|
+---------------------------------------------------------------------------------------------------------------+-------------------+---------------+-------+---------+
Model Information
| Model Name: | assertion_dl_radiology |
| Compatibility: | Spark NLP 2.7.4+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, chunk, embeddings] |
| Output Labels: | [assertion] |
| Language: | en |
Data Source
Custom internal labeled radiology dataset.
Benchmarking
label tp fp fn prec rec f1
Suspected 629 155 159 0.8022959 0.7982234 0.80025446
Negative 417 53 36 0.88723403 0.9205298 0.9035753
Confirmed 2252 173 186 0.9286598 0.92370796 0.92617726
tp: 3298 fp: 381 fn: 381 labels: 3
Macro-average prec: 0.87272996, rec: 0.88082033, f1: 0.8767565
Micro-average prec: 0.89643925, rec: 0.89643925, f1: 0.89643925