Description
This model extracts entities related to the patient’s response to the oncology treatment, including clinical response and changes in tumor size. It is the version of ner_oncology_response_to_treatment model augmented with langtest library.
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
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")\
.setSplitChars(['-'])
word_embeddings = WordEmbeddingsModel().pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"]) \
.setOutputCol("embeddings")
ner = MedicalNerModel.pretrained("ner_oncology_response_to_treatment_langtest", "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("document")
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
.setSplitChars(['-'])
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_langtest", "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)
Results
+----------+---------------------+
|chunk |ner_label |
+----------+---------------------+
|first-line|Line_Of_Therapy |
|recurrence|Response_To_Treatment|
+----------+---------------------+
Model Information
| Model Name: | ner_oncology_response_to_treatment_langtest |
| Compatibility: | Healthcare NLP 5.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 14.7 MB |
References
In-house annotated oncology case reports.
Benchmarking
label precision recall f1-score support
B-Response_To_Treatment 0.83 0.83 0.83 289
I-Response_To_Treatment 0.74 0.77 0.76 209
B-Size_Trend 0.65 0.69 0.67 101
I-Size_Trend 0.61 0.69 0.65 49
B-Line_Of_Therapy 1.00 0.97 0.99 36
I-Line_Of_Therapy 0.95 0.97 0.96 65
micro-avg 0.78 0.81 0.79 749
macro-avg 0.80 0.82 0.81 749
weighted-avg 0.78 0.81 0.79 749