Description
Relation extraction between body parts entities like ‘Internal_organ_or_component’, ’External_body_part_or_region’ etc. and procedure and test entities. 1 : body part and test/procedure are related to each other. 0 : body part and test/procedure are not related to each other.
Predicted Entities
0, 1
How to use
...
documenter = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentencer = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentences")
tokenizer = sparknlp.annotators.Tokenizer()\
.setInputCols(["sentences"])\
.setOutputCol("tokens")
pos_tagger = PerceptronModel()\
.pretrained("pos_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"])\
.setOutputCol("pos_tags")
words_embedder = WordEmbeddingsModel() \
.pretrained("embeddings_clinical", "en", "clinical/models") \
.setInputCols(["sentences", "tokens"]) \
.setOutputCol("embeddings")
ner_tagger = MedicalNerModel.pretrained("ner_jsl_greedy", "en", "clinical/models")\
.setInputCols("sentences", "tokens", "embeddings")\
.setOutputCol("ner_tags")
ner_converter = NerConverter() \
.setInputCols(["sentences", "tokens", "ner_tags"]) \
.setOutputCol("ner_chunks")
dependency_parser = DependencyParserModel() \
.pretrained("dependency_conllu", "en") \
.setInputCols(["sentences", "pos_tags", "tokens"]) \
.setOutputCol("dependencies")
# Set a filter on pairs of named entities which will be treated as relation candidates
re_ner_chunk_filter = RENerChunksFilter() \
.setInputCols(["ner_chunks", "dependencies"])\
.setMaxSyntacticDistance(10)\
.setOutputCol("re_ner_chunks")\
.setRelationPairs(["external_body_part_or_region-test"])
# The dataset this model is trained to is sentence-wise.
# This model can also be trained on document-level relations - in which case, while predicting, use "document" instead of "sentence" as input.
re_model = RelationExtractionDLModel()\
.pretrained('redl_bodypart_procedure_test_biobert', 'en', "clinical/models") \
.setPredictionThreshold(0.5)\
.setInputCols(["re_ner_chunks", "sentences"]) \
.setOutputCol("relations")
pipeline = Pipeline(stages=[documenter, sentencer, tokenizer, pos_tagger, words_embedder, ner_tagger, ner_converter, dependency_parser, re_ner_chunk_filter, re_model])
text ="TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound."
data = spark.createDataFrame([[text]]).toDF("text")
result = pipeline.fit(data).transform(data)
...
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentencer = new SentenceDetector()
.setInputCols(Array("document"))
.setOutputCol("sentences")
val tokenizer = new Tokenizer()
.setInputCols(Array("sentences"))
.setOutputCol("tokens")
val pos_tagger = PerceptronModel()
.pretrained("pos_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("pos_tags")
val words_embedder = WordEmbeddingsModel()
.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens"))
.setOutputCol("embeddings")
val ner_tagger = MedicalNerModel.pretrained("ner_jsl_greedy", "en", "clinical/models")
.setInputCols(Array("sentences", "tokens", "embeddings"))
.setOutputCol("ner_tags")
val ner_converter = new NerConverter()
.setInputCols(Array("sentences", "tokens", "ner_tags"))
.setOutputCol("ner_chunks")
val dependency_parser = DependencyParserModel()
.pretrained("dependency_conllu", "en")
.setInputCols(Array("sentences", "pos_tags", "tokens"))
.setOutputCol("dependencies")
// Set a filter on pairs of named entities which will be treated as relation candidates
val re_ner_chunk_filter = RENerChunksFilter()
.setInputCols(Array("ner_chunks", "dependencies"))
.setMaxSyntacticDistance(10)
.setOutputCol("re_ner_chunks")
.setRelationPairs(Array("external_body_part_or_region-test"))
// The dataset this model is trained to is sentence-wise.
// This model can also be trained on document-level relations - in which case, while predicting, use "document" instead of "sentence" as input.
val re_model = RelationExtractionDLModel()
.pretrained("redl_bodypart_procedure_test_biobert", "en", "clinical/models")
.setPredictionThreshold(0.5)
.setInputCols(Array("re_ner_chunks", "sentences"))
.setOutputCol("relations")
val pipeline = new Pipeline().setStages(Array(documenter, sentencer, tokenizer, pos_tagger, words_embedder, ner_tagger, ner_converter, dependency_parser, re_ner_chunk_filter, re_model))
val data = Seq("TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.relation.bodypart.procedure").predict("""TECHNIQUE IN DETAIL: After informed consent was obtained from the patient and his mother, the chest was scanned with portable ultrasound.""")
Results
| | relation | entity1 | chunk1 | entity2 | chunk2 | confidence |
|---:|-----------:|:-----------------------------|:---------|:----------|:--------------------|-------------:|
| 0 | 1 | External_body_part_or_region | chest | Test | portable ultrasound | 0.99953 |
Model Information
| Model Name: | redl_bodypart_procedure_test_biobert |
| Compatibility: | Healthcare NLP 3.0.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Case sensitive: | true |
Data Source
Trained on a custom internal dataset.
Benchmarking
Relation Recall Precision F1 Support
0 0.338 0.472 0.394 325
1 0.904 0.843 0.872 1275
Avg. 0.621 0.657 0.633 -