Description
This pretrained model maps abbreviations and acronyms of medical regulatory activities with their definition.
Important Note: Mappers extract additional information such as extended descriptions and categories related to Concept codes (such as RxNorm, ICD10, CPT, MESH, NDC, UMLS, etc.). They generally take Concept Codes, which are the outputs of EntityResolvers, as input. When creating a pipeline that contains ‘Mapper’, it is necessary to use the ChunkMapperModel after an EntityResolverModel.
Predicted Entities
definition
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols("sentence")\
.setOutputCol("token")
word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token"])\
.setOutputCol("embeddings")
#NER model to detect abbreviations in the text
abbr_ner = MedicalNerModel.pretrained("ner_abbreviation_clinical", "en", "clinical/models") \
.setInputCols(["sentence", "token", "embeddings"]) \
.setOutputCol("abbr_ner")
abbr_converter = NerConverter() \
.setInputCols(["sentence", "token", "abbr_ner"]) \
.setOutputCol("abbr_ner_chunk")\
chunkerMapper = ChunkMapperModel.pretrained("abbreviation_mapper", "en", "clinical/models")\
.setInputCols(["abbr_ner_chunk"])\
.setOutputCol("mappings")\
.setRel("definition")
pipeline = Pipeline().setStages([document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
abbr_ner,
abbr_converter,
chunkerMapper])
text = ["""Gravid with estimated fetal weight of 6-6/12 pounds.
LABORATORY DATA: Laboratory tests include a CBC which is normal.
HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet."""]
data = spark.createDataFrame([text]).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = new SentenceDetector()
.setInputCols(Array("document"))
.setOutputCol("sentence")
val tokenizer = new Tokenizer()
.setInputCols("sentence")
.setOutputCol("token")
val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token"))
.setOutputCol("embeddings")
val abbr_ner = MedicalNerModel.pretrained("ner_abbreviation_clinical", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("abbr_ner")
val abbr_converter = NerConverter()
.setInputCols(Array("sentence", "token", "abbr_ner"))
.setOutputCol("abbr_ner_chunk")
val chunkerMapper = ChunkMapperModel.pretrained("abbreviation_mapper", "en", "clinical/models")
.setInputCols(Array("abbr_ner_chunk"))
.setOutputCol("mappings")
.setRel("definition")
val pipeline = new Pipeline().setStages(Array(
document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
abbr_ner,
abbr_converter,
chunkerMapper))
val test_sentence = """Gravid with estimated fetal weight of 6-6/12 pounds.
LABORATORY DATA: Laboratory tests include a CBC which is normal.
HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet."""
val data = Seq(test_sentence).toDS.toDF("text")
val result= pipeline.fit(data).transform(data)
import nlu
nlu.load("en.map_entity.abbreviation_to_definition").predict("""Gravid with estimated fetal weight of 6-6/12 pounds.
LABORATORY DATA: Laboratory tests include a CBC which is normal.
HIV: Negative. One-Hour Glucose: 117. Group B strep has not been done as yet.""")
Results
+----------+------------------------------+
|ner_chunk |mapping_result |
+----------+------------------------------+
|CBC |complete blood count |
|HIV |human immunodeficiency virus |
+----------+------------------------------+
Model Information
| Model Name: | abbreviation_mapper |
| Compatibility: | Healthcare NLP 3.5.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [abbr_ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 214.8 KB |
References
https://www.johnsnowlabs.com/marketplace/list-of-abbreviations-and-acronyms-for-medical-regulatory-activities/