Pipeline to Mapping SNOMED Codes with Their Corresponding ICD10-CM Codes

Description

This pretrained pipeline is built on the top of snomed_icd10cm_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("snomed_icd10cm_mapping", "en", "clinical/models")

result = pipeline.fullAnnotate(["128041000119107", "292278006", "293072005"])

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("snomed_icd10cm_mapping", "en", "clinical/models")

val result = pipeline.fullAnnotate(["128041000119107", "292278006", "293072005"])

import nlu
nlu.load("en.map_entity.snomed_to_icd10cm.pipe").predict("""Put your text here.""")

Results

|   | icd10cm_code |     snomed_code |
|--:|-------------:|----------------:|
| 0 |       K22.70 | 128041000119107 |
| 1 |      T43.595 |       292278006 |
| 2 |      T37.1X5 |       293072005 |

Model Information

Included Models

• DocumentAssembler
• TokenizerModel
• ChunkMapperModel