Description
This pipeline can be used to extracts Consumption (Alcohol, Smoking/Tobaco, and Substance Usage) related information in medical text.
Alcohol refers to beverages containing ethanol, a psychoactive substance that is widely consumed for its pleasurable effects.
Smoking typically involves inhaling smoke from burning tobacco, a highly addictive substance.
Substance mentions of illegal recreational drugs use. Include also substances that can create dependency including here caffeine and tea. “overdose, cocaine, illicit substance intoxication, coffee, etc
Predicted Entities
CONSUMPTION
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("ner_consumption_benchmark_pipeline", "en", "clinical/models")
text = """SOCIAL HISTORY : The patient is a nonsmoker . Denies any alcohol or illicit drug use . The patient does live with his family .
SOCIAL HISTORY : The patient smokes approximately 2 packs per day times greater than 40 years . He does drink occasional alcohol approximately 5 to 6 alcoholic drinks per month . He denies any drug use . He is a retired liquor store owner .
SOCIAL HISTORY : Patient admits alcohol use , Drinking is described as heavy , Patient denies illegal drug use , Patient denies STD history , Patient denies tobacco use .
SOCIAL HISTORY : The patient is employed in the finance department . He is a nonsmoker . He does consume alcohol on the weekend as much as 3 to 4 alcoholic beverages per day on the weekends . He denies any IV drug use or abuse .
SOCIAL HISTORY : The patient is a smoker . Admits to heroin use , alcohol abuse as well . Also admits today using cocaine .
"""
result = ner_pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = nlp.PretrainedPipeline("ner_consumption_benchmark_pipeline", "en", "clinical/models")
text = """SOCIAL HISTORY : The patient is a nonsmoker . Denies any alcohol or illicit drug use . The patient does live with his family .
SOCIAL HISTORY : The patient smokes approximately 2 packs per day times greater than 40 years . He does drink occasional alcohol approximately 5 to 6 alcoholic drinks per month . He denies any drug use . He is a retired liquor store owner .
SOCIAL HISTORY : Patient admits alcohol use , Drinking is described as heavy , Patient denies illegal drug use , Patient denies STD history , Patient denies tobacco use .
SOCIAL HISTORY : The patient is employed in the finance department . He is a nonsmoker . He does consume alcohol on the weekend as much as 3 to 4 alcoholic beverages per day on the weekends . He denies any IV drug use or abuse .
SOCIAL HISTORY : The patient is a smoker . Admits to heroin use , alcohol abuse as well . Also admits today using cocaine .
"""
result = ner_pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("ner_consumption_benchmark_pipeline", "en", "clinical/models")
val text = """SOCIAL HISTORY : The patient is a nonsmoker . Denies any alcohol or illicit drug use . The patient does live with his family .
SOCIAL HISTORY : The patient smokes approximately 2 packs per day times greater than 40 years . He does drink occasional alcohol approximately 5 to 6 alcoholic drinks per month . He denies any drug use . He is a retired liquor store owner .
SOCIAL HISTORY : Patient admits alcohol use , Drinking is described as heavy , Patient denies illegal drug use , Patient denies STD history , Patient denies tobacco use .
SOCIAL HISTORY : The patient is employed in the finance department . He is a nonsmoker . He does consume alcohol on the weekend as much as 3 to 4 alcoholic beverages per day on the weekends . He denies any IV drug use or abuse .
SOCIAL HISTORY : The patient is a smoker . Admits to heroin use , alcohol abuse as well . Also admits today using cocaine .
"""
val result = ner_pipeline.fullAnnotate(text)
Results
| | chunk | begin | end | ner_label |
|---:|:--------------------|--------:|------:|:------------|
| 0 | nonsmoker | 34 | 42 | CONSUMPTION |
| 1 | alcohol | 57 | 63 | CONSUMPTION |
| 2 | illicit drug use | 68 | 83 | CONSUMPTION |
| 3 | smokes | 156 | 161 | CONSUMPTION |
| 4 | drink | 231 | 235 | CONSUMPTION |
| 5 | alcohol | 248 | 254 | CONSUMPTION |
| 6 | alcoholic drinks | 277 | 292 | CONSUMPTION |
| 7 | drug use | 320 | 327 | CONSUMPTION |
| 8 | alcohol use | 400 | 410 | CONSUMPTION |
| 9 | Drinking | 414 | 421 | CONSUMPTION |
| 10 | illegal drug use | 462 | 477 | CONSUMPTION |
| 11 | tobacco | 525 | 531 | CONSUMPTION |
| 12 | nonsmoker | 616 | 624 | CONSUMPTION |
| 13 | consume alcohol | 636 | 650 | CONSUMPTION |
| 14 | alcoholic beverages | 685 | 703 | CONSUMPTION |
| 15 | IV drug use | 745 | 755 | CONSUMPTION |
| 16 | smoker | 802 | 807 | CONSUMPTION |
| 17 | heroin use | 821 | 830 | CONSUMPTION |
| 18 | alcohol abuse | 834 | 846 | CONSUMPTION |
| 19 | using cocaine | 876 | 888 | CONSUMPTION |
Model Information
| Model Name: | ner_consumption_benchmark_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- WordEmbeddingsModel
- TextMatcherInternalModel
- TextMatcherInternalModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- ChunkMergeModel
- ChunkMergeModel
Benchmarking
label precision recall f1-score support
CONSUMPTION 0.988 0.977 0.983 662
O 1.000 1.000 1.000 81909
accuracy - - 1.000 82571
macro-avg 0.994 0.989 0.991 82571
weighted-avg 1.000 1.000 1.000 82571