Description
This model extracts entities related to the patient”s response to the oncology treatment, including clinical response and changes in tumor size.
Definitions of Predicted Entities:
Line_Of_Therapy: Explicit references to the line of therapy of an oncological therapy (e.g. “first-line treatment”).Response_To_Treatment: Terms related to clinical progress of the patient related to cancer treatment, including “recurrence”, “bad response” or “improvement”.Size_Trend: Terms related to the changes in the size of the tumor (such as “growth” or “reduced in size”).
Predicted Entities
Line_Of_Therapy, Response_To_Treatment, Size_Trend
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_response_to_treatment_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([["She completed her first-line therapy, but some months later there was recurrence of the breast cancer. "]]).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_response_to_treatment_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("She completed her first-line therapy, but some months later there was recurrence of the breast cancer. ").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.oncology_response_to_treatment_wip").predict("""She completed her first-line therapy, but some months later there was recurrence of the breast cancer. """)
Results
| chunk | ner_label |
|:-----------|:----------------------|
| first-line | Line_Of_Therapy |
| recurrence | Response_To_Treatment |
Model Information
| Model Name: | ner_oncology_response_to_treatment_wip |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 848.8 KB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Response_To_Treatment 233.0 81.0 120.0 353.0 0.74 0.66 0.70
Size_Trend 31.0 34.0 45.0 76.0 0.48 0.41 0.44
Line_Of_Therapy 82.0 11.0 5.0 87.0 0.88 0.94 0.91
macro_avg 346.0 126.0 170.0 516.0 0.70 0.67 0.68
micro_avg NaN NaN NaN NaN 0.73 0.67 0.70