New ICD10 and ICDO Pack: Embeddings, EntityResolver and TextMatcher models
Model or model pack description:
ICD10 and ICDO model pack:
| Model | name | language | loc |
|---|---|---|---|
| TextMatcherModel | textmatch_icdo_ner_n2c4 |
en | clinical/models |
| TextMatcherModel | textmatch_cpt_token_n2c1 |
en | clinical/models |
| WordEmbeddingsModel | embeddings_icdoem |
en | clinical/models |
| EntityResolutionModel | resolve_icd10cm_icdoem |
en | clinical/models |
| EntityResolutionModel | resolve_icdo_icdoem |
en | clinical/models |
| EntityResolutionModel | resolve_cpt_icdoem |
en | clinical/models |
| ChunkEntityResolutionModel | chunkresolve_icdo_icdoem |
en | clinical/models |
| ChunkEntityResolutionModel | chunkresolve_cpt_icdoem |
en | clinical/models |
The textmatch_icdo_ner_n2c4 and textmatch_cpt_token_n2c1 are Text Matching models trained from comprehensive glossaries for Oncology and Procedural terms
The embeddings_icdoem WordEmbeddingsModel, was trained with a semantically augmented corpus of clinical texts, case studies, and curated datasets.
The resolve_icd10cm_icdoem, resolve_icdo_icdoem and resolve_cpt_icdoem models are EntityResolvers trained with the embeddings_icdoem model and semantically augmented datasets from JSL Data Market
The chunkresolve_icdo_icdoem and chunkresolve_cpt_icdoem models are ChunkEntityResolvers that connect with the new ChunkEmbeddings annotator
Spark NLP:
- HEALTHCARE
Last update
26/11/2019
Notes
ChunkEntityResolutionApproach and ChunkEntityResolutionModel are new annotators coming in for Spark NLP 2.3.4.
The main difference with respect to EntityResolutionApproach and EntityResolutionModel is that they expect embeddings from ChunkEmbeddings. This makes WordEmbedding aggregation functions flexible for chunks.
Works with
Spark NLP 2.3.4 and above
Link
Examples on how to use these models can be found here:
Notebooks
Healthcare Notebooks