Description
This pretrained pipeline maps entities (Clinical Findings) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
Open in Colab Download Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
text = '''['HTG-induced pancreatitis associated with an acute hepatitis, and obesity']'''
result = pipeline.annotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("umls_clinical_findings_resolver_pipeline", "en", "clinical/models")
val text = "HTG-induced pancreatitis associated with an acute hepatitis, and obesity"
val result = pipeline.annotate(text)
import nlu
nlu.load("en.map_entity.umls_clinical_findings_resolver").predict("""['HTG-induced pancreatitis associated with an acute hepatitis, and obesity']""")
Results
+------------------------+---------+---------+
|chunk |ner_label|umls_code|
+------------------------+---------+---------+
|HTG-induced pancreatitis|PROBLEM |C1963198 |
|an acute hepatitis |PROBLEM |C4750596 |
|obesity |PROBLEM |C1963185 |
+------------------------+---------+---------+
Model Information
| Model Name: | umls_clinical_findings_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 4.3 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger