Description
This model extracts mentions of anatomical entities using granular labels. It is the version of ner_oncology_anatomy_granular model augmented with langtest library.
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
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_langtest", "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_langtest", "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)
Results
+------+-----------+
|chunk |ner_label |
+------+-----------+
|left |Direction |
|breast|Site_Breast|
|lungs |Site_Lung |
|liver |Site_Liver |
+------+-----------+
Model Information
| Model Name: | ner_oncology_anatomy_granular_langtest |
| Compatibility: | Healthcare NLP 5.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.7 MB |
References
In-house annotated oncology case reports.
Benchmarking
label precision recall f1-score support
B-Direction 0.86 0.94 0.90 870
B-Site_Bone 0.85 0.82 0.83 247
B-Site_Lymph_Node 0.86 0.86 0.86 239
I-Site_Lymph_Node 0.89 0.88 0.88 331
B-Site_Other_Body_Part 0.78 0.76 0.77 1045
I-Site_Other_Body_Part 0.66 0.72 0.69 529
B-Site_Brain 0.86 0.85 0.86 184
I-Site_Brain 0.80 0.74 0.77 70
B-Site_Lung 0.82 0.89 0.85 361
I-Site_Lung 0.76 0.75 0.76 167
I-Site_Bone 0.80 0.71 0.75 106
I-Direction 0.74 0.85 0.79 84
B-Site_Breast 0.90 0.96 0.93 117
B-Site_Liver 0.81 0.89 0.85 168
I-Site_Liver 0.50 0.62 0.55 52
I-Site_Breast 0.93 0.76 0.84 17
micro-avg 0.80 0.83 0.81 4587
macro-avg 0.80 0.81 0.80 4587
weighted-avg 0.80 0.83 0.81 4587