Description
This model detects drugs, dosage, form, frequency, duration, route, and drug strength in text. It differs from ner_posology in the sense that it chunks together drugs, dosage, form, strength, and route when they appear together, resulting in a bigger chunk. It is trained using embeddings_clinical so please use the same embeddings in the pipeline.
Predicted Entities
DRUG, STRENGTH, DURATION, FREQUENCY, FORM, DOSAGE, ROUTE.
Live Demo Open in Colab Copy S3 URI
How to use
Use as part of an nlp pipeline with the following stages: DocumentAssembler, SentenceDetector, Tokenizer, WordEmbeddingsModel, NerDLModel. Add the NerConverter to the end of the pipeline to convert entity tokens into full entity chunks.
...
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
clinical_ner = NerDLModel.pretrained("ner_posology_greedy", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("ner")
...
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter])
model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
results = model.transform(spark.createDataFrame([["The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day."]]).toDF("text"))
...
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val clinical_ner = NerDLModel.pretrained("ner_posology_greedy", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner")
...
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter))
val data = Seq("The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.med_ner.posology.greedy").predict("""The patient was prescribed 1 capsule of Advil 10 mg for 5 days and magnesium hydroxide 100mg/1ml suspension PO. He was seen by the endocrinology service and she was discharged on 40 units of insulin glargine at night, 12 units of insulin lispro with meals, and metformin 1000 mg two times a day.""")
Results
+----+----------------------------------+---------+-------+------------+
| | chunks | begin | end | entities |
|---:|---------------------------------:|--------:|------:|-----------:|
| 0 | 1 capsule of Advil 10 mg | 27 | 50 | DRUG |
| 1 | magnesium hydroxide 100mg/1ml PO | 67 | 98 | DRUG |
| 2 | for 5 days | 52 | 61 | DURATION |
| 3 | 40 units of insulin glargine | 168 | 195 | DRUG |
| 4 | at night | 197 | 204 | FREQUENCY |
| 5 | 12 units of insulin lispro | 207 | 232 | DRUG |
| 6 | with meals | 234 | 243 | FREQUENCY |
| 7 | metformin 1000 mg | 250 | 266 | DRUG |
| 8 | two times a day | 268 | 282 | FREQUENCY |
+----+----------------------------------+---------+-------+------------+
Model Information
| Model Name: | ner_posology_greedy |
| Type: | ner |
| Compatibility: | Spark NLP 2.6.5+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence, token, embeddings] |
| Output Labels: | [ner] |
| Language: | en |
| Dependencies: | embeddings_clinical |
Data Source
Trained on augmented version of i2b2_med7 + FDA dataset with embeddings_clinical, https://www.i2b2.org/NLP/Medication.
Benchmarking
| label | tp | fp | fn | prec | rec | f1 |
|---|---|---|---|---|---|---|
| B-DRUG | 29362 | 1679 | 1985 | 0.9459103 | 0.93667656 | 0.94127077 |
| B-STRENGTH | 14018 | 1172 | 864 | 0.922844 | 0.9419433 | 0.9322958 |
| I-DURATION | 6404 | 935 | 476 | 0.87259847 | 0.93081397 | 0.9007666 |
| I-STRENGTH | 16686 | 1991 | 1292 | 0.8933983 | 0.9281344 | 0.9104351 |
| I-FREQUENCY | 19743 | 1088 | 1081 | 0.9477702 | 0.94808877 | 0.9479294 |
| B-FORM | 2733 | 526 | 780 | 0.8386008 | 0.7779676 | 0.80714715 |
| B-DOSAGE | 2774 | 474 | 688 | 0.85406405 | 0.80127096 | 0.8268257 |
| I-DOSAGE | 1357 | 490 | 844 | 0.7347049 | 0.6165379 | 0.67045456 |
| I-DRUG | 37846 | 4103 | 3386 | 0.90219074 | 0.91787934 | 0.9099674 |
| I-ROUTE | 208 | 30 | 62 | 0.8739496 | 0.77037036 | 0.8188976 |
| B-ROUTE | 3061 | 340 | 451 | 0.9000294 | 0.87158316 | 0.88557786 |
| B-DURATION | 2491 | 388 | 276 | 0.865231 | 0.900253 | 0.8823946 |
| B-FREQUENCY | 13065 | 608 | 436 | 0.9555328 | 0.9677061 | 0.9615809 |
| I-FORM | 154 | 69 | 386 | 0.69058293 | 0.2851852 | 0.40366974 |
| Macro-average | 149902 | 13893 | 13007 | 0.8712434 | 0.82817215 | 0.849162 |
| Micro-average | 149902 | 13893 | 13007 | 0.91518056 | 0.92015785 | 0.9176625 |