Description
This pretrained pipeline maps entities (Clinical Drugs) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
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How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline= PretrainedPipeline("umls_drug_resolver_pipeline", "en", "clinical/models")
val pipeline.annotate("The patient was given Adapin 10 MG, coumadn 5 mg")
import nlu
nlu.load("en.map_entity.umls_drug_resolver").predict("""The patient was given Adapin 10 MG, coumadn 5 mg""")
Results
+------------+---------+---------+
|chunk |ner_label|umls_code|
+------------+---------+---------+
|Adapin 10 MG|DRUG |C2930083 |
|coumadn 5 mg|DRUG |C2723075 |
+------------+---------+---------+
Model Information
| Model Name: | umls_drug_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 4.6 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger