Description
This pretrained pipeline is built on the top of ner_nihss model.
Predicted Entities
Available as Private API Endpoint
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
text = '''Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_nihss_pipeline", "en", "clinical/models")
val text = "Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently"
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.nihss_pipeline").predict("""Abdomen , soft , nontender . NIH stroke scale on presentation was 23 to 24 for , one for consciousness , two for month and year and two for eye / grip , one to two for gaze , two for face , eight for motor , one for limited ataxia , one to two for sensory , three for best language and two for attention . On the neurologic examination the patient was intermittently""")
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-----------------|--------:|------:|:----------------|-------------:|
| 0 | NIH stroke scale | 29 | 44 | NIHSS | 0.973533 |
| 1 | 23 to 24 | 66 | 73 | Measurement | 0.870567 |
| 2 | one | 81 | 83 | Measurement | 0.8726 |
| 3 | consciousness | 89 | 101 | 1a_LOC | 0.6322 |
| 4 | two | 105 | 107 | Measurement | 0.9665 |
| 5 | month and year | 113 | 126 | 1b_LOCQuestions | 0.846433 |
| 6 | two | 132 | 134 | Measurement | 0.9659 |
| 7 | eye / grip | 140 | 149 | 1c_LOCCommands | 0.889433 |
| 8 | one | 153 | 155 | Measurement | 0.9917 |
| 9 | two | 160 | 162 | Measurement | 0.5144 |
| 10 | gaze | 168 | 171 | 2_BestGaze | 0.7272 |
| 11 | two | 175 | 177 | Measurement | 0.9872 |
| 12 | face | 183 | 186 | 4_FacialPalsy | 0.8758 |
| 13 | eight | 190 | 194 | Measurement | 0.9013 |
| 14 | one | 208 | 210 | Measurement | 0.9343 |
| 15 | limited | 216 | 222 | 7_LimbAtaxia | 0.9326 |
| 16 | ataxia | 224 | 229 | 7_LimbAtaxia | 0.5762 |
| 17 | one to two | 233 | 242 | Measurement | 0.79 |
| 18 | sensory | 248 | 254 | 8_Sensory | 0.9892 |
| 19 | three | 258 | 262 | Measurement | 0.8896 |
| 20 | best language | 268 | 280 | 9_BestLanguage | 0.89415 |
| 21 | two | 286 | 288 | Measurement | 0.949 |
Model Information
| Model Name: | ner_nihss_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel