Description
Extracts biomarker entities using rule based TextMatcherInternal annotator.
Predicted Entities
Biomarker
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(["document"])\
.setOutputCol("token")
text_matcher = TextMatcherInternalModel.pretrained("biomarker_matcher","en","clinical/models") \
.setInputCols(["document", "token"])\
.setOutputCol("matched_text")\
mathcer_pipeline = Pipeline().setStages([
documentAssembler,
tokenizer,
text_matcher])
data = spark.createDataFrame([["In the bone- marrow (BM) aspiration, blasts accounted for 88.1% of ANCs, which were positive for CD20, CD34, CD38, CD58, CD66c, CD123, HLA-DR, cCD79a, and TdT on flow cytometry. Measurements of serum tumor markers showed elevated level of cytokeratin 19 fragment (Cyfra21-1: 4.77 ng/mL), neuron-specific enolase (NSE: 19.60 ng/mL), and squamous cell carcinoma antigen (SCCA: 2.58 ng/mL)."]]).toDF("text")
matcher_model = mathcer_pipeline.fit(data)
result = matcher_model.transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val text_matcher = TextMatcherInternalModel.pretrained("biomarker_matcher","en","clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("matched_text")
val mathcer_pipeline = new Pipeline()
.setStages(Array(documentAssembler,
tokenizer,
text_matcher))
val data = Seq("In the bone- marrow (BM) aspiration, blasts accounted for 88.1% of ANCs, which were positive for CD20, CD34, CD38, CD58, CD66c, CD123, HLA-DR, cCD79a, and TdT on flow cytometry. Measurements of serum tumor markers showed elevated level of cytokeratin 19 fragment (Cyfra21-1: 4.77 ng/mL), neuron-specific enolase (NSE: 19.60 ng/mL), and squamous cell carcinoma antigen (SCCA: 2.58 ng/mL).") .toDF("text")
val matcher_model = mathcer_pipeline.fit(data)
val result = matcher_model.transform(data)
Results
+-------------------------------+-----+---+---------+
| chunk|begin|end| label|
+-------------------------------+-----+---+---------+
| CD20| 97|100|Biomarker|
| CD34| 103|106|Biomarker|
| CD38| 109|112|Biomarker|
| CD58| 115|118|Biomarker|
| CD66c| 121|125|Biomarker|
| CD123| 128|132|Biomarker|
| HLA-DR| 135|140|Biomarker|
| cCD79a| 143|148|Biomarker|
| TdT| 155|157|Biomarker|
| cytokeratin 19 fragment| 239|261|Biomarker|
| Cyfra21-1| 264|272|Biomarker|
| neuron-specific enolase| 288|310|Biomarker|
| NSE| 313|315|Biomarker|
|squamous cell carcinoma antigen| 336|366|Biomarker|
| SCCA| 369|372|Biomarker|
+-------------------------------+-----+---+---------+
Model Information
| Model Name: | biomarker_matcher |
| Compatibility: | Healthcare NLP 5.3.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [matched_text] |
| Language: | en |
| Size: | 26.2 KB |
| Case sensitive: | false |