Description
This pretrained medical spellchecker pipeline is built on the top of spellcheck_clinical model.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
example = ["Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
"With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
"Abdomen is sort, nontender, and nonintended.",
"Patient not showing pain or any wealth problems.",
"No cute distress"]
pipeline.fullAnnotate(example)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("spellcheck_clinical_pipeline", "en", "clinical/models")
val example = Array("Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.",
"With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.",
"Abdomen is sort, nontender, and nonintended.",
"Patient not showing pain or any wealth problems.",
"No cute distress")
pipeline.fullAnnotate(example)
import nlu
nlu.load("en.spell.clinical.pipeline").predict("""Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.""")
Results
[{'checked': ['With','the','cell','of','physical','therapy','the','patient','was','ambulated','and','on','postoperative',',','the','patient','tolerating','a','post','surgical','soft','diet','.'],
'document': ['Witth the hell of phisical terapy the patient was imbulated and on postoperative, the impatient tolerating a post curgical soft diet.'],
'token': ['Witth','the','hell','of','phisical','terapy','the','patient','was','imbulated','and','on','postoperative',',','the','impatient','tolerating','a','post','curgical','soft','diet','.']},
{'checked': ['With','pain','well','controlled','on','oral','pain','medications',',','she','was','discharged','to','rehabilitation','facility','.'],
'document': ['With paint wel controlled on orall pain medications, she was discharged too reihabilitation facilitay.'],
'token': ['With','paint','wel','controlled','on','orall','pain','medications',',','she','was','discharged','too','reihabilitation','facilitay','.']},
{'checked': ['Abdomen','is','soft',',','nontender',',','and','nondistended','.'],
'document': ['Abdomen is sort, nontender, and nonintended.'],
'token': ['Abdomen','is','sort',',','nontender',',','and','nonintended','.']},
{'checked': ['Patient','not','showing','pain','or','any','health','problems','.'],
'document': ['Patient not showing pain or any wealth problems.'],
'token': ['Patient','not','showing','pain','or','any','wealth','problems','.']},
{'checked': ['No', 'acute', 'distress'],
'document': ['No cute distress'],
'token': ['No', 'cute', 'distress']}]
Model Information
| Model Name: | spellcheck_clinical_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 141.2 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- ContextSpellCheckerModel