Description
This model extracts cancer therapies (Cancer_Surgery, Radiotherapy and Cancer_Therapy) and posology information at a granular level.
Definitions of Predicted Entities:
Cancer_Surgery: Terms that indicate surgery as a form of cancer treatment.Cancer_Therapy: Any cancer treatment mentioned in text, excluding surgeries and radiotherapy.Cycle_Count: The total number of cycles being administered of an oncological therapy (e.g. “5 cycles”).Cycle_Day: References to the day of the cycle of oncological therapy (e.g. “day 5”).Cycle_Number: The number of the cycle of an oncological therapy that is being applied (e.g. “third cycle”).Dosage: The quantity prescribed by the physician for an active ingredient.Duration: Words indicating the duration of a treatment (e.g. “for 2 weeks”).Frequency: Words indicating the frequency of treatment administration (e.g. “daily” or “bid”).Radiotherapy: Terms that indicate the use of Radiotherapy.Radiation_Dose: Dose used in radiotherapy.Route: Words indicating the type of administration route (such as “PO” or “transdermal”).
Predicted Entities
Cancer_Surgery, Cancer_Therapy, Cycle_Count, Cycle_Day, Cycle_Number, Dosage, Duration, Frequency, Radiotherapy, Radiation_Dose, Route
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_posology_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([["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(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_posology_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("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_posology_wip").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 | Dosage |
| cyclophosphamide | Cancer_Therapy |
| 600 mg/m2 | Dosage |
| six courses | Cycle_Count |
| second cycle | Cycle_Number |
| chemotherapy | Cancer_Therapy |
Model Information
| Model Name: | ner_oncology_posology_wip |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Size: | 856.4 KB |
References
In-house annotated oncology case reports.
Benchmarking
label tp fp fn total precision recall f1
Cycle_Number 56.0 12.0 17.0 73.0 0.82 0.77 0.79
Cycle_Count 148.0 44.0 27.0 175.0 0.77 0.85 0.81
Radiotherapy 185.0 2.0 37.0 222.0 0.99 0.83 0.90
Cancer_Surgery 494.0 59.0 151.0 645.0 0.89 0.77 0.82
Cycle_Day 144.0 22.0 39.0 183.0 0.87 0.79 0.83
Frequency 270.0 17.0 79.0 349.0 0.94 0.77 0.85
Route 67.0 5.0 30.0 97.0 0.93 0.69 0.79
Cancer_Therapy 1093.0 74.0 165.0 1258.0 0.94 0.87 0.90
Duration 316.0 43.0 231.0 547.0 0.88 0.58 0.70
Dosage 703.0 16.0 124.0 827.0 0.98 0.85 0.91
Radiation_Dose 84.0 14.0 12.0 96.0 0.86 0.88 0.87
macro_avg 3560.0 308.0 912.0 4472.0 0.90 0.78 0.83
micro_avg NaN NaN NaN NaN 0.92 0.80 0.85