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", "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("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_test", "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").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 guided thick-needle | Pathology_Test |
| chest computed tomography | Imaging_Test |
| CT | Imaging_Test |
Model Information
| Model Name: | ner_oncology_test |
| Compatibility: | Healthcare NLP 4.2.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 34.2 MB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Imaging_Test 2020 229 184 2204 0.90 0.92 0.91
Biomarker_Result 1177 186 268 1445 0.86 0.81 0.84
Pathology_Test 888 276 162 1050 0.76 0.85 0.80
Biomarker 1287 254 228 1515 0.84 0.85 0.84
Oncogene 365 89 84 449 0.80 0.81 0.81
macro_avg 5737 1034 926 6663 0.83 0.85 0.84
micro_avg 5737 1034 926 6663 0.85 0.86 0.85