Description
This pretrained pipeline maps ICD10 codes to ICD9 codes without using any text data. You’ll just feed a comma or white space-delimited ICD10 codes and it will return the corresponding ICD9 codes as a list.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
pipeline.annotate('E669 R630 J988')
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("icd10_icd9_mapping", "en", "clinical/models")
val result = pipeline.annotate('E669 R630 J988')
import nlu
nlu.load("en.icd10_icd9.mapping").predict("""E669 R630 J988""")
Results
{'document': ['E669 R630 J988'],
'icd10': ['E669', 'R630', 'J988'],
'icd9': ['27800', '7830', '5198']}
Note:
| ICD10 | Details |
| ---------- | ----------------------------:|
| E669 | Obesity |
| R630 | Anorexia |
| J988 | Other specified respiratory disorders |
| ICD9 | Details |
| ---------- | ---------------------------:|
| 27800 | Obesity |
| 7830 | Anorexia |
| 5198 | Other diseases of respiratory system |
Model Information
| Model Name: | icd10_icd9_mapping |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.3.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 545.2 KB |
Included Models
- DocumentAssembler
- TokenizerModel
- LemmatizerModel