Pipeline for Detect Medication

Description

A pretrained pipeline to detect medication entities. It was built on the top of ner_posology_greedy model and also augmented with the drug names mentioned in UK and US drugbank datasets.

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How to use

from sparknlp.pretrained import PretrainedPipeline

ner_medication_pipeline = PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")

text = """The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."""

result = ner_medication_pipeline.fullAnnotate([text])

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val ner_medication_pipeline = new PretrainedPipeline("ner_medication_pipeline", "en", "clinical/models")

val result = ner_medication_pipeline.fullAnnotate("The patient was prescribed metformin 1000 MG, and glipizide 2.5 MG. The other patient was given Fragmin 5000 units, Xenaderm to wounds topically b.i.d. and OxyContin 30 mg."")

| ner_chunk          | entity   |
|:-------------------|:---------|
| metformin 1000 MG  | DRUG     |
| glipizide 2.5 MG   | DRUG     |
| Fragmin 5000 units | DRUG     |
| Xenaderm           | DRUG     |
| OxyContin 30 mg    | DRUG     |

Model Information

Included Models

• DocumentAssembler
• SentenceDetectorDLModel
• TokenizerModel
• WordEmbeddingsModel
• MedicalNerModel
• NerConverterInternalModel
• TextMatcherModel
• ChunkMergeModel
• Finisher