Description
This pretrained pipeline maps entities (Drug Substances) 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_substance_resolver_pipeline", "en", "clinical/models")
pipeline.annotate("The patient was given metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline= PretrainedPipeline("umls_drug_substance_resolver_pipeline", "en", "clinical/models")
val pipeline.annotate("The patient was given metformin, lenvatinib and Magnesium hydroxide 100mg/1ml")
import nlu
nlu.load("en.map_entity.umls_drug_substance_resolver").predict("""The patient was given metformin, lenvatinib and Magnesium hydroxide 100mg/1ml""")
Results
+-----------------------------+---------+---------+
|chunk |ner_label|umls_code|
+-----------------------------+---------+---------+
|metformin |DRUG |C0025598 |
|lenvatinib |DRUG |C2986924 |
|Magnesium hydroxide 100mg/1ml|DRUG |C1134402 |
+-----------------------------+---------+---------+
Model Information
| Model Name: | umls_drug_substance_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 5.1 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger