Description
A pipeline for detecting posology entities with the ner_posology_large NER model, assigning their assertion status with assertion_oncology_wip model, and extracting relations between posology-related terminology with posology_re relation extraction model.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
result = ner_pipeline.annotate("""The patient is a 30-year-old female with diabetes mellitus type 2. She received a course of Bactrim for 14 days for UTI.
She was prescribed 5000 units of Fragmin subcutaneously daily. She was also prescribed 40 units of Lantus subcutaneously at night.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("explain_clinical_doc_medication", "en", "clinical/models")
val result = ner_pipeline.annotate("""The patient is a 30-year-old female with diabetes mellitus type 2. She received a course of Bactrim for 14 days for UTI.
She was prescribed 5000 units of Fragmin subcutaneously daily. She was also prescribed 40 units of Lantus subcutaneously at night.""")
Results
# ner_chunk
+--------------+-----+---+---------+
| ner_chunk|begin|end|ner_label|
+--------------+-----+---+---------+
| Bactrim| 92| 98| DRUG|
| for 14 days| 100|110| DURATION|
| 5000 units| 141|150| DOSAGE|
| Fragmin| 155|161| DRUG|
|subcutaneously| 163|176| ROUTE|
| daily| 178|182|FREQUENCY|
| 40 units| 209|216| DOSAGE|
| Lantus| 221|226| DRUG|
|subcutaneously| 228|241| ROUTE|
| at night| 243|250|FREQUENCY|
+--------------+-----+---+---------+
# assertion
+-------+-----+---+--------+---------+-----------+
| chunks|begin|end|entities|assertion|confidence)|
+-------+-----+---+--------+---------+-----------+
|Bactrim| 92| 98| DRUG| Past| 0.9324|
|Fragmin| 155|161| DRUG| Present| 0.7456|
| Lantus| 190|195| DRUG| Present| 0.4984|
+-------+-----+---+--------+---------+-----------+
# relation
+--------+--------------+---------+----------+-------+--------------+---------+----------+
|sentence| relation|direction| chunk1|entity1| chunk2| entity2|confidence|
+--------+--------------+---------+----------+-------+--------------+---------+----------+
| 1| DRUG-DURATION| both| Bactrim| DRUG| for 14 days| DURATION| 1.0|
| 2| DOSAGE-DRUG| both|5000 units| DOSAGE| Fragmin| DRUG| 1.0|
| 2| DRUG-ROUTE| both| Fragmin| DRUG|subcutaneously| ROUTE| 1.0|
| 2|DRUG-FREQUENCY| both| Fragmin| DRUG| daily|FREQUENCY| 1.0|
| 3| DOSAGE-DRUG| both| 40 units| DOSAGE| Lantus| DRUG| 1.0|
| 3| DRUG-ROUTE| both| Lantus| DRUG|subcutaneously| ROUTE| 1.0|
| 3|DRUG-FREQUENCY| both| Lantus| DRUG| at night|FREQUENCY| 1.0|
+--------+--------------+---------+----------+-------+--------------+---------+----------+
Model Information
| Model Name: | explain_clinical_doc_medication |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.3.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- NerConverterInternalModel
- AssertionDLModel
- PerceptronModel
- DependencyParserModel
- PosologyREModel