Description
This model extracts mentions of tests from oncology texts, including pathology tests and imaging tests.
Definitions of Predicted Entities:
Biomarker: Biological molecules that indicate the presence or absence of cancer, or the type of cancer. Oncogenes are excluded from this category.Biomarker_Result: Terms or values that are identified as the result of a biomarkers.Imaging_Test: Imaging tests mentioned in texts, such as “chest CT scan”.Oncogene: Mentions of genes that are implicated in the etiology of cancer.Pathology_Test: Mentions of biopsies or tests that use tissue samples.
Predicted Entities
Biomarker, Biomarker_Result, Imaging_Test, Oncogene, Pathology_Test
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_test_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([["A biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative."]]).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_test_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("A biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.oncology_test_wip").predict("""A biopsy was conducted using an ultrasound guided thick-needle. His chest computed tomography (CT) scan was negative.""")
Results
| chunk | ner_label |
|:--------------------------|:---------------|
| biopsy | Pathology_Test |
| ultrasound | Imaging_Test |
| chest computed tomography | Imaging_Test |
| CT | Imaging_Test |
Model Information
| Model Name: | ner_oncology_test_wip |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 858.2 KB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Imaging_Test 1518.0 156.0 191.0 1709.0 0.91 0.89 0.90
Biomarker_Result 861.0 145.0 245.0 1106.0 0.86 0.78 0.82
Pathology_Test 600.0 105.0 209.0 809.0 0.85 0.74 0.79
Biomarker 917.0 166.0 194.0 1111.0 0.85 0.83 0.84
Oncogene 274.0 84.0 83.0 357.0 0.77 0.77 0.77
macro_avg 4170.0 656.0 922.0 5092.0 0.85 0.80 0.82
micro_avg NaN NaN NaN NaN 0.86 0.82 0.84