Description
A pipeline for detecting posology entities with the ner_posology_large NER model, assigning their assertion status with assertion_jsl model, and extracting relations between posology-related terminology with posology_re relation extraction model.
Predicted Entities
DRUG, DOSAGE, DURATION, FORM, FREQUENCY, ROUTE, STRENGTH
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
text = '''The patient is a 30-year-old female with an insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI. She was prescribed 5000 units of Fragmin subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
val text = "The patient is a 30-year-old female with an insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI. She was prescribed 5000 units of Fragmin subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.explain_dco.clinical_medication.pipeline").predict("""The patient is a 30-year-old female with an insulin dependent diabetes, type 2. She received a course of Bactrim for 14 days for UTI. She was prescribed 5000 units of Fragmin subcutaneously daily, and along with Lantus 40 units subcutaneously at bedtime.""")
Results
# ner_chunk
| | ner_chunk | begin | end | ner_label |
|---:|---------------:|------:|----:|----------:|
| 0 | insulin | 44 | 50 | DRUG |
| 1 | Bactrim | 105 | 111 | DRUG |
| 2 | for 14 days | 113 | 123 | DURATION |
| 3 | 5000 units | 153 | 162 | DOSAGE |
| 4 | Fragmin | 167 | 173 | DRUG |
| 5 | subcutaneously | 176 | 189 | ROUTE |
| 6 | daily | 191 | 195 | FREQUENCY |
| 7 | Lantus | 213 | 218 | DRUG |
| 8 | 40 units | 220 | 227 | DOSAGE |
| 9 | subcutaneously | 229 | 242 | ROUTE |
| 10 | at bedtime | 244 | 253 | FREQUENCY |
# assertion
| | chunks | entities | assertion |
|--:|--------:|---------:|----------:|
| 0 | insulin | DRUG | Present |
| 1 | Bactrim | DRUG | Past |
| 2 | Fragmin | DRUG | Planned |
| 3 | Lantus | DRUG | Past |
# relation
| | relation | entity1 | chunk1 | entity2 | chunk2 |
|--:|---------------:|--------:|-----------:|----------:|---------------:|
| 0 | DRUG-DURATION | DRUG | Bactrim | DURATION | for 14 days |
| 1 | DOSAGE-DRUG | DOSAGE | 5000 units | DRUG | Fragmin |
| 2 | DRUG-ROUTE | DRUG | Fragmin | ROUTE | subcutaneously |
| 3 | DRUG-FREQUENCY | DRUG | Fragmin | FREQUENCY | daily |
| 4 | DRUG-DOSAGE | DRUG | Lantus | DOSAGE | 40 units |
| 5 | DRUG-ROUTE | DRUG | Lantus | ROUTE | subcutaneously |
| 6 | DRUG-FREQUENCY | DRUG | Lantus | FREQUENCY | at bedtime |
Model Information
| Model Name: | explain_clinical_doc_medication |
| 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
- NerConverterInternalModel
- AssertionDLModel
- PerceptronModel
- DependencyParserModel
- PosologyREModel