Pipeline to Resolve ICD-10-CM Codes

Description

This pretrained pipeline maps entities with their corresponding ICD-10-CM codes. You’ll just feed your text and it will return the corresponding ICD-10-CM codes.

Predicted Entities

TREATMENT, PROBLEM,TEST

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Available as Private API Endpoint

Deploy

Deploy

Deploy

How to use

from sparknlp.pretrained import PretrainedPipeline

resolver_pipeline = PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")

text = """A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage"""

result = resolver_pipeline.fullAnnotate(text)

import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val resolver_pipeline = new PretrainedPipeline("icd10cm_resolver_pipeline", "en", "clinical/models")

val result = resolver_pipeline.fullAnnotate("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")

import nlu
nlu.load("en.icd10cm_resolver.pipeline").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years and anisakiasis. Also, it was reported that fetal and neonatal hemorrhage""")

Results

|   |                        chunks | entities | icd10cm_code |
|--:|------------------------------:|---------:|-------------:|
| 0 | gestational diabetes mellitus |  PROBLEM |      O24.919 |
| 1 |                   anisakiasis |  PROBLEM |        B81.0 |
| 2 | fetal and neonatal hemorrhage |  PROBLEM |         P549 |

Model Information

Included Models

• DocumentAssembler
• SentenceDetector
• TokenizerModel
• WordEmbeddingsModel
• MedicalNerModel
• NerConverter
• ChunkMapperModel
• ChunkMapperModel
• ChunkMapperFilterer
• Chunk2Doc
• BertSentenceEmbeddings
• SentenceEntityResolverModel
• ResolverMerger