Description
This model extracts anatomical entities using an unspecific label.
Definitions of Predicted Entities:
Anatomical_Site: Relevant anatomical terms mentioned in text.Direction: Directional and laterality terms, such as “left”, “right”, “bilateral”, “upper” and “lower”.
Predicted Entities
Anatomical_Site, Direction
Live Demo Open in Colab Copy S3 URI
How to use
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")
ner = MedicalNerModel.pretrained("ner_oncology_anatomy_general_wip", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")
pipeline = Pipeline(stages=[document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
ner,
ner_converter])
data = spark.createDataFrame([["The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver."]]).toDF("text")
result = pipeline.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(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentence"))
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val ner = MedicalNerModel.pretrained("ner_oncology_anatomy_general_wip", "en", "clinical/models")
.setInputCols(Array("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,
word_embeddings,
ner,
ner_converter))
val data = Seq("The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.oncology_anatomy_general").predict("""The patient presented a mass in her left breast, and a possible metastasis in her lungs and in her liver.""")
Results
| chunk | ner_label |
|:--------|:----------------|
| left | Direction |
| breast | Anatomical_Site |
| lungs | Anatomical_Site |
| liver | Anatomical_Site |
Model Information
| Model Name: | ner_oncology_anatomy_general_wip |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 843.0 KB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Anatomical_Site 2377.0 649.0 353.0 2730.0 0.79 0.87 0.83
Direction 668.0 219.0 66.0 734.0 0.75 0.91 0.82
macro_avg 3045.0 868.0 419.0 3464.0 0.77 0.89 0.83
micro_avg NaN NaN NaN NaN 0.78 0.88 0.83