Description
This model extracts mentions of biomarkers and biomarker results from oncology texts.
Definitions of Predicted Entities:
Biomarker: Biological molecules that indicate the presence or absence of cancer, or the type of cancer (including oncogenes).Biomarker_Result: Terms or values that are identified as the result of a biomarkers.
Predicted Entities
Biomarker, Biomarker_Result
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_biomarker", "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 results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%."]]).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_biomarker", "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 results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.oncology_biomarker").predict("""The results of immunohistochemical examination showed that she tested negative for CK7, synaptophysin (Syn), chromogranin A (CgA), Muc5AC, human epidermal growth factor receptor-2 (HER2), and Muc6; positive for CK20, Muc1, Muc2, E-cadherin, and p53; the Ki-67 index was about 87%.""")
Results
| chunk | ner_label |
|:-----------------------------------------|:-----------------|
| negative | Biomarker_Result |
| CK7 | Biomarker |
| synaptophysin | Biomarker |
| Syn | Biomarker |
| chromogranin A | Biomarker |
| CgA | Biomarker |
| Muc5AC | Biomarker |
| human epidermal growth factor receptor-2 | Biomarker |
| HER2 | Biomarker |
| Muc6 | Biomarker |
| positive | Biomarker_Result |
| CK20 | Biomarker |
| Muc1 | Biomarker |
| Muc2 | Biomarker |
| E-cadherin | Biomarker |
| p53 | Biomarker |
| Ki-67 index | Biomarker |
| 87% | Biomarker_Result |
Model Information
| Model Name: | ner_oncology_biomarker |
| 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
Biomarker_Result 1030 148 415 1445 0.87 0.71 0.79
Biomarker 1685 272 279 1964 0.86 0.86 0.86
macro_avg 2715 420 694 3409 0.87 0.79 0.82
micro_avg 2715 420 694 3409 0.87 0.80 0.83