Description
Pretrained named entity recognition deep learning model for clinical terms. The SparkNLP deep learning model (NerDL) is inspired by a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM-CNN.
Predicted Entities
PROBLEM, TEST, TREATMENT.
Live Demo Open in Colab Download
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.
...
clinical_ner = NerDLModel.pretrained("ner_clinical_large", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
...
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])
model = nlpPipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(data)
...
val ner = NerDLModel.pretrained("ner_clinical_large", "en", "clinical/models")
.setInputCols("sentence", "token", "embeddings")
.setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, ner, ner_converter))
val result = pipeline.fit(Seq.empty[String].toDS.toDF("text")).transform(data)
Results
The output is a dataframe with a sentence per row and a "ner" column containing all of the entity labels in the sentence, entity character indices, and other metadata. To get only the tokens and entity labels, without the metadata, select "token.result" and "ner.result" from your output dataframe:
+-----------------------------------------------------------+---------+
|chunk |ner_label|
+-----------------------------------------------------------+---------+
|the G-protein-activated inwardly rectifying potassium (GIRK|TREATMENT|
|the genomicorganization |TREATMENT|
|a candidate gene forType II diabetes mellitus |PROBLEM |
|byapproximately |TREATMENT|
|single nucleotide polymorphisms |TREATMENT|
|aVal366Ala substitution |TREATMENT|
|an 8 base-pair |TREATMENT|
|insertion/deletion |PROBLEM |
|Ourexpression studies |TEST |
|the transcript in various humantissues |PROBLEM |
|fat andskeletal muscle |PROBLEM |
|furtherstudies |PROBLEM |
|the KCNJ9 protein |TREATMENT|
|evaluation |TEST |
|Type II diabetes |PROBLEM |
|the treatment |TREATMENT|
|breast cancer |PROBLEM |
|the standard therapy |TREATMENT|
|anthracyclines |TREATMENT|
|taxanes |TREATMENT|
+-----------------------------------------------------------+---------+
Model Information
| Model Name: | ner_clinical_large |
| Type: | ner |
| Compatibility: | Spark NLP 2.5.0+ |
| Edition: | Official |
| License: | Licenced |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | [en] |
| Case sensitive: | false |
Data Source
Trained on augmented 2010 i2b2 challenge data with ‘embeddings_clinical’. https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
Benchmarking
| | label | tp | fp | fn | prec | rec | f1 |
|---:|--------------:|------:|------:|------:|---------:|---------:|---------:|
| 0 | I-TREATMENT | 6625 | 1187 | 1329 | 0.848054 | 0.832914 | 0.840416 |
| 1 | I-PROBLEM | 15142 | 1976 | 2542 | 0.884566 | 0.856254 | 0.87018 |
| 2 | B-PROBLEM | 11005 | 1065 | 1587 | 0.911765 | 0.873968 | 0.892466 |
| 3 | I-TEST | 6748 | 923 | 1264 | 0.879677 | 0.842237 | 0.86055 |
| 4 | B-TEST | 8196 | 942 | 1029 | 0.896914 | 0.888455 | 0.892665 |
| 5 | B-TREATMENT | 8271 | 1265 | 1073 | 0.867345 | 0.885167 | 0.876165 |
| 6 | Macro-average | 55987 | 7358 | 8824 | 0.881387 | 0.863166 | 0.872181 |
| 7 | Micro-average | 55987 | 7358 | 8824 | 0.883842 | 0.86385 | 0.873732 |