Drug Strength Contextual Parser Pipeline

Description

This pipeline, identifies drug strength entities in clinical text. It recognizes dosage patterns including mg, mcg, g, ml, IU, units, and various numeric formats.

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How to use


from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("drug_strength_parser_pipeline", "en", "clinical/models")

sample_text = """ Patient was prescribed Metformin 500mg twice daily and Lisinopril 10mg once daily. Ibuprofen 200mg PRN for pain. Vitamin D 1000 IU daily was also recommended."""

result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))


from johnsnowlabs import nlp, medical

pipeline = nlp.PretrainedPipeline("drug_strength_parser_pipeline", "en", "clinical/models")

sample_text = """ Patient was prescribed Metformin 500mg twice daily and Lisinopril 10mg once daily. Ibuprofen 200mg PRN for pain. Vitamin D 1000 IU daily was also recommended."""

result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))


import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = PretrainedPipeline("drug_strength_parser_pipeline", "en", "clinical/models")

val sample_text = """ Patient was prescribed Metformin 500mg twice daily and Lisinopril 10mg once daily. Ibuprofen 200mg PRN for pain. Vitamin D 1000 IU daily was also recommended."""

val result = pipeline.transform(spark.createDataFrame([[sample_text]]).toDF("text"))

Results


|   chunk |   begin |   end | label         |
|--------:|--------:|------:|:--------------|
| 500mg   |      33 |    37 | DRUG_STRENGTH |
| 10mg    |      66 |    69 | DRUG_STRENGTH |
| 200mg   |      93 |    97 | DRUG_STRENGTH |
| 1000 IU |     123 |   129 | DRUG_STRENGTH |

Model Information

Model Name: drug_strength_parser_pipeline
Type: pipeline
Compatibility: Healthcare NLP 6.3.0+
License: Licensed
Edition: Official
Language: en
Size: 401.8 KB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • ContextualParserModel
  • ChunkConverter