Description
This model extractions mentions of anatomical entities using granular labels.
Definitions of Predicted Entities:
Direction: Directional and laterality terms, such as “left”, “right”, “bilateral”, “upper” and “lower”.Site_Bone: Anatomical terms that refer to the human skeleton.Site_Brain: Anatomical terms that refer to the central nervous system (including the brain stem and the cerebellum).Site_Breast: Anatomical terms that refer to the breasts.Site_Liver: Anatomical terms that refer to the liver.Site_Lung: Anatomical terms that refer to the lungs.Site_Lymph_Node: Anatomical terms that refer to lymph nodes, excluding adenopathies.Site_Other_Body_Part: Relevant anatomical terms that are not included in the rest of the anatomical entities.
Predicted Entities
Direction, Site_Bone, Site_Brain, Site_Breast, Site_Liver, Site_Lung, Site_Lymph_Node, Site_Other_Body_Part
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_granular_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_granular_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_granular_wip").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 | Site_Breast |
| lungs | Site_Lung |
| liver | Site_Liver |
Model Information
| Model Name: | ner_oncology_anatomy_granular_wip |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 859.9 KB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Direction 601.0 150.0 133.0 734.0 0.80 0.82 0.81
Site_Lymph_Node 415.0 31.0 51.0 466.0 0.93 0.89 0.91
Site_Breast 98.0 6.0 20.0 118.0 0.94 0.83 0.88
Site_Other_Body_Part 713.0 277.0 388.0 1101.0 0.72 0.65 0.68
Site_Bone 176.0 30.0 56.0 232.0 0.85 0.76 0.80
Site_Liver 134.0 77.0 36.0 170.0 0.64 0.79 0.70
Site_Lung 337.0 70.0 106.0 443.0 0.83 0.76 0.79
Site_Brain 164.0 58.0 36.0 200.0 0.74 0.82 0.78
macro_avg 2638.0 699.0 826.0 3464.0 0.81 0.79 0.80
micro_avg NaN NaN NaN NaN 0.79 0.76 0.78