Extract Cancer Therapies and Posology Information

Description

This model extracts mentions of treatments and posology information using unspecific labels (low granularity).

Predicted Entities

Posology_Information, Cancer_Therapy

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_healthcare_100d", "en", "clinical/models")\
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("embeddings")                

ner = MedicalNerModel\
    .pretrained("ner_oncology_unspecific_posology_healthcare", "en", "clinical/models") \
    .setInputCols(["sentence", "token", "embeddings"]) \
    .setOutputCol("ner")

ner_converter = NerConverterInternal() \
    .setInputCols(["sentence", "token", "ner"]) \
    .setOutputCol("ner_chunk")
        
pipeline = Pipeline(stages=[document_assembler,
                            sentence_detector,
                            tokenizer,
                            word_embeddings,
                            ner,
                            ner_converter])

data = spark.createDataFrame([["The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition."]]).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_healthcare_100d", "en", "clinical/models")
    .setInputCols(Array("sentence", "token"))
    .setOutputCol("embeddings")                
    
val ner = MedicalNerModel.pretrained("ner_oncology_unspecific_posology_healthcare", "en", "clinical/models")
    .setInputCols(Array("sentence", "token", "embeddings"))
    .setOutputCol("ner")
    
val ner_converter = new NerConverterInternal()
    .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 patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.oncology_unspecific_posology_healthcare").predict("""The patient underwent a regimen consisting of adriamycin (60 mg/m2) and cyclophosphamide (600 mg/m2) over six courses. She is currently receiving his second cycle of chemotherapy and is in good overall condition.""")

Results

| chunk            | ner_label            |
|:-----------------|:---------------------|
| adriamycin       | Cancer_Therapy       |
| 60 mg/m2         | Posology_Information |
| cyclophosphamide | Cancer_Therapy       |
| 600 mg/m2        | Posology_Information |
| over six courses | Posology_Information |
| second cycle     | Posology_Information |
| chemotherapy     | Cancer_Therapy       |

Model Information

Model Name: ner_oncology_unspecific_posology_healthcare
Compatibility: Healthcare NLP 4.2.4+
License: Licensed
Edition: Official
Input Labels: [sentence, token, embeddings]
Output Labels: [ner]
Language: en
Size: 33.8 MB

References

In-house annotated oncology case reports.

Benchmarking

               label   tp  fp  fn  total  precision  recall   f1
Posology_Information 1435 102 210   1645       0.93    0.87 0.90
      Cancer_Therapy 1281 116 125   1406       0.92    0.91 0.91
           macro-avg 2716 218 335   3051       0.93    0.89 0.91
           micro-avg 2716 218 335   3051       0.93    0.89 0.91