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", "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("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("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", "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").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 |
| Compatibility: | Spark NLP for Healthcare 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 34.3 MB |
| Dependencies: | embeddings_clinical |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Direction 822 221 162 984 0.79 0.84 0.81
Site_Lymph_Node 481 38 70 551 0.93 0.87 0.90
Site_Breast 88 14 59 147 0.86 0.60 0.71
Site_Other_Body_Part 604 184 897 1501 0.77 0.40 0.53
Site_Bone 252 74 61 313 0.77 0.81 0.79
Site_Liver 178 92 56 234 0.66 0.76 0.71
Site_Lung 398 98 161 559 0.80 0.71 0.75
Site_Brain 197 44 82 279 0.82 0.71 0.76
macro_avg 3020 765 1548 4568 0.80 0.71 0.74
micro_avg 3020 765 1548 4568 0.80 0.66 0.71