# Relation Extraction Model Clinical

## Description

Relation Extraction model based on syntactic features using deep learning Models the set of clinical relations defined in the 2010 i2b2 relation challenge.

## Predicted Entities

TrIP: A certain treatment has improved or cured a medical problem (eg, ‘infection resolved with antibiotic course’) TrWP: A patient’s medical problem has deteriorated or worsened because of or in spite of a treatment being administered (eg, ‘the tumor was growing despite the drain’) TrCP: A treatment caused a medical problem (eg, ‘penicillin causes a rash’) TrAP: A treatment administered for a medical problem (eg, ‘Dexamphetamine for narcolepsy’) TrNAP: The administration of a treatment was avoided because of a medical problem (eg, ‘Ralafen which is contra-indicated because of ulcers’) TeRP: A test has revealed some medical problem (eg, ‘an echocardiogram revealed a pericardial effusion’) TeCP: A test was performed to investigate a medical problem (eg, ‘chest x-ray done to rule out pneumonia’) PIP: Two problems are related to each other (eg, ‘Azotemia presumed secondary to sepsis’)

## How to use

reModel = RelationExtractionModel.pretrained("re_clinical","en","clinical/models")\
.setInputCols("word_embeddings","chunk","pos","dependency")\
.setOutput

pipeline = Pipeline(stages=[
documenter,
sentencer,
tokenizer,
words_embedder,
pos_tagger,
ner_tagger,
ner_chunker,
dependency_parser,
reModel
])
model = pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))

results = sparknlp.base.LightPipeline(model).fullAnnotate("""The patient was prescribed 1 unit of Advil for 5 days after meals. The patient was also
given 1 unit of Metformin daily.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.""")

val model = RelationExtractionModel.pretrained("re_clinical","en","clinical/models")
.setInputCols("word_embeddings","chunk","pos","dependency")
.setOutputCol("category")


## Results

+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
|   |       relation | entity1 | entity1_begin | entity1_end |           chunk1 |   entity2 | entity2_begin | entity2_end |           chunk2 | confidence |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 0 |    DOSAGE-DRUG |  DOSAGE |            28 |          33 |           1 unit |      DRUG |            38 |          42 |            Advil |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 1 |  DRUG-DURATION |    DRUG |            38 |          42 |            Advil |  DURATION |            44 |          53 |       for 5 days |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 2 |    DOSAGE-DRUG |  DOSAGE |            96 |         101 |           1 unit |      DRUG |           106 |         114 |        Metformin |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 3 | DRUG-FREQUENCY |    DRUG |           106 |         114 |        Metformin | FREQUENCY |           116 |         120 |            daily |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 4 |    DOSAGE-DRUG |  DOSAGE |           190 |         197 |         40 units |      DRUG |           202 |         217 | insulin glargine |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 5 | DRUG-FREQUENCY |    DRUG |           202 |         217 | insulin glargine | FREQUENCY |           219 |         226 |         at night |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 6 |    DOSAGE-DRUG |  DOSAGE |           231 |         238 |         12 units |      DRUG |           243 |         256 |   insulin lispro |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 7 | DRUG-FREQUENCY |    DRUG |           243 |         256 |   insulin lispro | FREQUENCY |           258 |         267 |       with meals |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 8 |  DRUG-STRENGTH |    DRUG |           275 |         283 |        metformin |  STRENGTH |           285 |         291 |          1000 mg |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+
| 9 | DRUG-FREQUENCY |    DRUG |           275 |         283 |        metformin | FREQUENCY |           293 |         307 |  two times a day |        1.0 |
+---+----------------+---------+---------------+-------------+------------------+-----------+---------------+-------------+------------------+------------+


## Model Information

 Name: re_clinical Type: RelationExtractionModel Compatibility: Spark NLP 2.5.5+ License: Licensed Edition: Official Input labels: [word_embeddings, chunk, pos, dependency] Output labels: [category] Language: en Case sensitive: False Dependencies: embeddings_clinical

## Data Source

Trained on data gathered and manually annotated by John Snow Labs https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/

## Benchmarking

The model has been validated agains the posology dataset described in (Magge, Scotch, & Gonzalez-Hernandez, 2018).

+----------------+--------+-----------+------+------------------------------------------------+
|    Relation    | Recall | Precision |  F1  | F1 (Magge, Scotch, & Gonzalez-Hernandez, 2018) |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-ADE       | 0.66   | 1.00      | 0.80 | 0.76                                           |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-DOSAGE    | 0.89   | 1.00      | 0.94 | 0.91                                           |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-DURATION  | 0.75   | 1.00      | 0.85 | 0.92                                           |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-FORM      | 0.88   | 1.00      | 0.94 | 0.95*                                          |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-FREQUENCY | 0.79   | 1.00      | 0.88 | 0.90                                           |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-REASON    | 0.60   | 1.00      | 0.75 | 0.70                                           |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-ROUTE     | 0.79   | 1.00      | 0.88 | 0.95*                                          |
+----------------+--------+-----------+------+------------------------------------------------+
| DRUG-STRENGTH  | 0.95   | 1.00      | 0.98 | 0.97                                           |
+----------------+--------+-----------+------+------------------------------------------------+


*Magge, Scotch, Gonzalez-Hernandez (2018) collapsed DRUG-FORM and DRUG-ROUTE into a single relation.