Pipeline to Mapping MESH Codes with Their Corresponding UMLS Codes

Description

This pretrained pipeline is built on the top of mesh_umls_mapper model and maps MESH codes to corresponding UMLS codes under the Unified Medical Language System.

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How to use

from sparknlp.pretrained import PretrainedPipeline

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

sample_text = """ [['C000015'], ['C000002']]"""

result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))

from johnsnowlabs import nlp, medical

pipeline = nlp.PretrainedPipeline("mesh_umls_mapping", "en", "clinical/models")

sample_text = """ [['C000015'], ['C000002']]"""

result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

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

val sample_text = """ [['C000015'], ['C000002']]"""

val result = pipeline.transform(spark.createDataFrame(sample_text).toDF("text"))

Results

| mesh_code | umls_code |
| :-------- | :-------- |
| C000015   | C0067655  |
| C000002   | C0950157  |

Model Information

Included Models

• DocumentAssembler
• Doc2Chunk
• ChunkMapperModel