Description
This pretrained pipeline maps entities (Clinical Major Concepts) with their corresponding UMLS CUI codes. You’ll just feed your text and it will return the corresponding UMLS codes.
Predicted Entities
Qualitative_Concept, Mental_Process, Health_Care_Activity, Professional_or_Occupational_Group, Population_Group, Group,Pharmacologic_Substance, Research_Activity, Medical_Device, Diagnostic_Procedure, Molecular_Function, Spatial_Concept, Organic_Chemical, Amino_Acid, Peptide_or_Protein, Disease_or_Syndrome, Daily_or_Recreational_Activity, Quantitative_Concept, Biologic_Function, Organism_Attribute, Clinical_Attribute, Pathologic_Function, Eukaryote,Body_Part,Organ_or_Organ_Component,Anatomical_Structure,Cell_Component,Geographic_Area,Manufactured_Object,Tissue,Plant,Nucleic_Acid,Nucleoside_or_Nucleotide,Indicator,Reagent_or_Diagnostic_Aid,Prokaryote,Chemical,Therapeutic_or_Preventive_Procedure,Gene_or_Genome,Mammal,Laboratory_Procedure,Substance,Molecular_Biology_Research_Technique,Neoplastic_Process,Cell,Food,Genetic_Function,Mental_or_Behavioral_Dysfunction,Body_Substance,Sign_or_Symptom,Injury_or_Poisoning,Body_Location_or_Region,Organization,Body_System,Fungus,Virus,Nucleotide_Sequence,Biomedical_or_Dental_Material
Open in Colab Download Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline= PretrainedPipeline("umls_major_concepts_resolver_pipeline", "en", "clinical/models")
val pipeline.annotate("The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician")
import nlu
nlu.load("en.map_entity.umls_major_concepts_resolver").predict("""The patient complains of pustules after falling from stairs. She has been advised Arthroscopy by her primary care pyhsician""")
Results
+-----------+-----------------------------------+---------+
|chunk |ner_label |umls_code|
+-----------+-----------------------------------+---------+
|pustules |Sign_or_Symptom |C0241157 |
|stairs |Daily_or_Recreational_Activity |C4300351 |
|Arthroscopy|Therapeutic_or_Preventive_Procedure|C0179144 |
+-----------+-----------------------------------+---------+
Model Information
| Model Name: | umls_major_concepts_resolver_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 3.0 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- ChunkMapperModel
- ChunkMapperModel
- ChunkMapperFilterer
- Chunk2Doc
- BertSentenceEmbeddings
- SentenceEntityResolverModel
- ResolverMerger