Description
This pretrained pipeline maps entities (Diseases and Syndromes) 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_disease_syndrome_resolver_pipeline", "en", "clinical/models")
pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline= PretrainedPipeline("umls_disease_syndrome_resolver_pipeline", "en", "clinical/models")
val pipeline.annotate("A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria")
import nlu
nlu.load("en.map_entity.umls_disease_syndrome_resolver").predict("""A 34-year-old female with a history of poor appetite, gestational diabetes mellitus, acyclovir allergy and polyuria""")
Results
+-----------------------------+---------+---------+
|chunk |ner_label|umls_code|
+-----------------------------+---------+---------+
|poor appetite |PROBLEM |C0003123 |
|gestational diabetes mellitus|PROBLEM |C0085207 |
|acyclovir allergy |PROBLEM |C0571297 |
|polyuria |PROBLEM |C0018965 |
+-----------------------------+---------+---------+
Model Information
| Model Name: | umls_disease_syndrome_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.4 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- MedicalNerModel
- NerConverter
- ChunkMergeModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger