Description
Entity Resolution model Based on KNN using Word Embeddings + Word Movers Distance.
Predicted Entities
ICD10-PCS Codes and their normalized definition with clinical_embeddings.
Live Demo Open in Colab Copy S3 URI
How to use
...
model = ChunkEntityResolverModel.pretrained("chunkresolve_icd10pcs_clinical","en","clinical/models")
.setInputCols("token","chunk_embeddings")
.setOutputCol("entity")
pipeline_icd10pcs = Pipeline(stages = [documentAssembler, sentenceDetector, tokenizer, stopwords, word_embeddings, ner, chunk_embeddings, model])
data = ["""He has a starvation ketosis but nothing found for significant for dry oral mucosa"""]
pipeline_model = pipeline_icd10pcs.fit(spark.createDataFrame([[""]]).toDF("text"))
light_pipeline = LightPipeline(pipeline_model)
result = light_pipeline.annotate(data)
...
val model = ChunkEntityResolverModel.pretrained("chunkresolve_icd10pcs_clinical","en","clinical/models")
.setInputCols("token","chunk_embeddings")
.setOutputCol("entity")
val pipeline = new Pipeline().setStages(Array(documentAssembler, sentenceDetector, tokenizer, stopwords, word_embeddings, ner, chunk_embeddings, model))
val data = Seq("He has a starvation ketosis but nothing found for significant for dry oral mucosa").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| | chunks | begin | end | code | resolutions |
|---|----------------------|-------|-----|---------|--------------------------------------------------|
| 0 | a starvation ketosis | 7 | 26 | 6A3Z1ZZ | Hyperthermia, Multiple:::Narcosynthesis:::Hype...|
| 1 | dry oral mucosa | 66 | 80 | 8E0ZXY4 | Yoga Therapy:::Release Cecum, Open Approach:::...|
Model Information
| Model Name: | chunkresolve_icd10pcs_clinical |
| Compatibility: | Healthcare NLP 3.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [token, chunk_embeddings] |
| Output Labels: | [icd10pcs] |
| Language: | en |