Description
Detect Drug, Dosage and administration instructions in text using pretraiend NER model.
Predicted Entities
Drug, Duration, Strength, Form, Frequency, Dosage, 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")
embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_healthcare_100d", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
clinical_ner = MedicalNerModel.pretrained("ner_posology_healthcare", "en", "clinical/models")\
.setInputCols(["sentence", "token", "embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("entities")
pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings_clinical, clinical_ner, ner_converter])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually."]]).toDF("text"))
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 embeddings_clinical = WordEmbeddingsModel.pretrained("embeddings_healthcare_100d", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val clinical_ner = MedicalNerModel.pretrained("ner_posology_healthcare", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence", "token", "ner"))
.setOutputCol("entities")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings_clinical, ner, ner_converter))
val text = """The patient is a 40-year-old white male who presents with a chief complaint of 'chest pain'. The patient is diabetic and has a prior history of coronary artery disease. The patient presents today stating that chest pain started yesterday evening. He has been advised Aspirin 81 milligrams QDay. insulin 50 units in a.m. HCTZ 50 mg QDay. Nitroglycerin 1/150 sublingually."""
val data = Seq(text).toDS.toDF("text")
val results = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.posology.healthcare").predict("""Put your text here.""")
Results
+-------------+---------+
|chunk |ner_label|
+-------------+---------+
|Aspirin |Drug |
|81 milligrams|Strength |
|QDay |Frequency|
|insulin |Drug |
|50 units |Dosage |
|in a.m. |Frequency|
|HCTZ |Drug |
|50 mg |Strength |
|QDay |Frequency|
|Nitroglycerin|Drug |
|1/150 |Strength |
|sublingually.|Route |
+-------------+---------+
Model Information
| Model Name: | ner_posology_healthcare |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
Benchmarking
label tp fp fn total precision recall f1
DURATION 995.0 463.0 132.0 1127.0 0.6824 0.8829 0.7698
DRUG 4957.0 632.0 476.0 5433.0 0.8869 0.9124 0.8995
DOSAGE 539.0 183.0 380.0 919.0 0.7465 0.5865 0.6569
ROUTE 676.0 47.0 129.0 805.0 0.935 0.8398 0.8848
FREQUENCY 3688.0 675.0 313.0 4001.0 0.8453 0.9218 0.8819
FORM 1328.0 261.0 294.0 1622.0 0.8357 0.8187 0.8272
STRENGTH 5008.0 687.0 557.0 5565.0 0.8794 0.8999 0.8895
macro-avg - - - - - - 0.82994
micro-avg - - - - - - 0.86743