Description
This pipeline is designed to extract medication entities in generic form from texts.
2 NER models and a text matcher are used to extract the medication entities.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("explain_clinical_doc_medication_generic_light", "en", "clinical/models")
result = ner_pipeline.annotate("""In response, his doctor prescribed a regimen tailored to his conditions:
Thiamine 100 mg q.day , Folic acid 1 mg q.day , multivitamins q.day , Calcium carbonate plus Vitamin D 250 mg t.i.d. , Heparin 5000 units subcutaneously b.i.d. , Prilosec 20 mg q.day , Senna two tabs qhs.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("explain_clinical_doc_medication_generic_light", "en", "clinical/models")
val result = ner_pipeline.annotate("""In response, his doctor prescribed a regimen tailored to his conditions:
Thiamine 100 mg q.day , Folic acid 1 mg q.day , multivitamins q.day , Calcium carbonate plus Vitamin D 250 mg t.i.d. , Heparin 5000 units subcutaneously b.i.d. , Prilosec 20 mg q.day , Senna two tabs qhs.""")
Results
| | chunks | begin | end | entities |
|---:|:----------------------------------|--------:|------:|:-----------|
| 0 | Thiamine 100 mg | 73 | 87 | DRUG |
| 1 | q.day | 89 | 93 | FREQUENCY |
| 2 | Folic acid 1 mg | 97 | 111 | DRUG |
| 3 | q.day | 113 | 117 | FREQUENCY |
| 4 | multivitamins | 121 | 133 | DRUG |
| 5 | q.day | 135 | 139 | FREQUENCY |
| 6 | Calcium carbonate | 143 | 159 | DRUG |
| 7 | Vitamin D 250 mg | 166 | 181 | DRUG |
| 8 | t.i.d | 183 | 187 | FREQUENCY |
| 9 | Heparin 5000 units subcutaneously | 192 | 224 | DRUG |
| 10 | b.i.d | 226 | 230 | FREQUENCY |
| 11 | Prilosec 20 mg | 235 | 248 | DRUG |
| 12 | q.day | 250 | 254 | FREQUENCY |
| 13 | Senna two tabs | 258 | 271 | DRUG |
| 14 | qhs | 273 | 275 | FREQUENCY |
Model Information
| Model Name: | explain_clinical_doc_medication_generic_light |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 6.0.2+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel
- MedicalNerModel
- NerConverterInternalModel
- TextMatcherInternalModel
- ChunkMergeModel