Pipeline to Mapping MESH Codes with Their Corresponding UMLS Codes

Description

This pretrained pipeline is built on the top of mesh_umls_mapper model.

Predicted Entities

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Available as Private API Endpoint

Deploy

Deploy

Deploy

How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")
result = pipeline.fullAnnotate(["C028491", "D019326", "C579867"])

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")

val result = pipeline.fullAnnotate(["C028491", "D019326", "C579867"])

import nlu
nlu.load("en.mesh.umls.mapping").predict("""Put your text here.""")

Results

|   | mesh_code | umls_code |
|--:|----------:|----------:|
| 0 |   C028491 |  C0043904 |
| 1 |   D019326 |  C0045010 |
| 2 |   C579867 |  C3696376 |

Model Information

Included Models

• DocumentAssembler
• TokenizerModel
• ChunkMapperModel