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