Description
Assertion of Clinical Entities based on Deep Learning.
Predicted Entities
hypothetical, present, absent, possible, conditional, associated_with_someone_else
How to use
model = AssertionDLModel.pretrained("assertion_dl_healthcare","en","clinical/models")\
.setInputCols("document","chunk","word_embeddings")\
.setOutputCol("assertion")
val model = AssertionDLModel.pretrained("assertion_dl_healthcare","en","clinical/models")
.setInputCols("document","chunk","word_embeddings")
.setOutputCol("assertion")
Model Information
| Name: | assertion_dl_healthcare | |
| Type: | AssertionDLModel | |
| Compatibility: | 2.6.0 | |
| License: | Licensed | |
| Edition: | Official | |
| Input labels: | [document, chunk, word_embeddings] | |
| Output labels: | [assertion] | |
| Language: | en | |
| Case sensitive: | False | |
| Dependencies: | embeddings_healthcare_100d |
Data Source
Trained on 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text with embeddings_clinical
https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/