Description
Run the pipeline
Predicted Entities
Lymph_Node, Histological_Type, Anatomical_Site, Staging, Imaging_Test, Tumor_Finding, Invasion, Response_To_Treatment, Adverse_Event, Tumor_Size, Biomarker_Result, Clinical_Condition, Chemotherapy, ICD10_Code, Targeted_Therapy, Radiotherapy, Performance_Status, Pathology_Test, Supportive_Care, Cancer_Surgery, Pathology_Result, Hormonal_Therapy, Biomarker, Immunotherapy, Metastasis, Cancer_Dx, Grade, Date, Treatment_Intent
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_cancer_registry", "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([[""]]).toDF("text")
model = pipeline.fit(data)
Model Information
| Model Name: | ner_cancer_registry |
| Compatibility: | Healthcare NLP 6.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.8 MB |