NER Pipeline for Temporal Mentions - Voice of the Patient

Description

This pipeline extracts mentions of temporal entities from health-related text in colloquial language.

Predicted Entities

Live Demo Open in Colab Copy S3 URI

How to use

from sparknlp.pretrained import PretrainedPipeline

pipeline = PretrainedPipeline("ner_vop_temporal_pipeline", "en", "clinical/models")

pipeline.annotate("
I broke my arm playing football last month and had to get surgery in the orthopedic department. The cast just came off yesterday and I'm excited to start physical therapy and get back to the game.")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val pipeline = new PretrainedPipeline("ner_vop_temporal_pipeline", "en", "clinical/models")

val result = pipeline.annotate("
I broke my arm playing football last month and had to get surgery in the orthopedic department. The cast just came off yesterday and I'm excited to start physical therapy and get back to the game.")

Results

| chunk      | ner_label   |
|:-----------|:------------|
| last month | DateTime    |
| yesterday  | DateTime    |

Model Information

Model Name: ner_vop_temporal_pipeline
Type: pipeline
Compatibility: Healthcare NLP 4.4.4+
License: Licensed
Edition: Official
Language: en
Size: 791.6 MB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • WordEmbeddingsModel
  • MedicalNerModel
  • NerConverter