Description
This pipeline detects drugs, dosage, form, frequency, duration, route, and drug strength in text.
Predicted entities are
DRUG, STRENGTH, DURATION, FREQUENCY, FORM, DOSAGE, ROUTE.
Live Demo Open in Colab Download
How to use
from sparknlp.pretrained import PretrainedPipelinein
pipeline = PretrainedPipeline('recognize_entities_posology', lang = 'en')
annotations = pipeline.fullAnnotate(""The patient was perscriped 50MG penicilin for is headache"")[0]
annotations.keys()
val pipeline = new PretrainedPipeline("recognize_entities_posology", lang = "en")
val result = pipeline.fullAnnotate("The patient was perscriped 50MG penicilin for is headache")(0)
import nlu
result_df = nlu.load('ner.posology').predict("The patient was perscriped 50MG penicilin for is headache")
result_df
Results
+-----------------------------------------+
|result |
+-----------------------------------------+
|[O, O, O, O, B-Strength, B-Drug, O, O, O]|
+-----------------------------------------+
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|ner |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[[named_entity, 0, 2, O, [word -> The, confidence -> 1.0], []], [named_entity, 4, 10, O, [word -> patient, confidence -> 0.9993], []], [named_entity, 12, 14, O, [word -> was, confidence -> 1.0], []], [named_entity, 16, 25, O, [word -> perscriped, confidence -> 0.9985], []], [named_entity, 27, 30, B-Strength, [word -> 50MG, confidence -> 0.9966], []], [named_entity, 32, 40, B-Drug, [word -> penicilin, confidence -> 0.9934], []], [named_entity, 42, 44, O, [word -> for, confidence -> 0.9999], []], [named_entity, 46, 47, O, [word -> is, confidence -> 0.9468], []], [named_entity, 49, 56, O, [word -> headache, confidence -> 0.9805], []]]|
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | recognize_entities_posology |
| Type: | pipeline |
| Compatibility: | Spark NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- NerDLModel
- NerConverter