Description
This model maps entities extracted from text to their corresponding LOINC (Logical Observation Identifiers Names and Codes) codes.
It performs a direct lookup against the training dictionary, providing fast, exact-match code mapping.
Trained on the augmented version of the LOINC 2.83 dataset (LOINC 2.83 official data plus an in-house curated dataset), scoped to numeric LOINC codes, without the inclusion of LOINC’s non-numeric Part, Answer, Panel, Survey, and other auxiliary/document codes.
If you also need LOINC’s non-numeric auxiliary codes, use loinc_mapper_2_83 instead.
Live Demo Open in Colab Copy S3 URI
How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetectorDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
.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_radiology = MedicalNerModel.pretrained("ner_radiology","en","clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner_radiology")
ner_converter_radiology = NerConverterInternal()\
.setInputCols(["sentence","token","ner_radiology"])\
.setOutputCol("ner_chunk_radiology")\
.setWhiteList(["Test"])
ner_jsl = MedicalNerModel.pretrained("ner_jsl","en","clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner_jsl")
ner_converter_jsl = NerConverterInternal()\
.setInputCols(["sentence","token","ner_jsl"])\
.setOutputCol("ner_chunk_jsl")\
.setWhiteList(["Test"])
chunk_merger = ChunkMergeApproach()\
.setInputCols("ner_chunk_jsl", "ner_chunk_radiology")\
.setOutputCol("ner_chunk")
loinc_mapper = ChunkMapperModel.pretrained("loinc_numeric_mapper_2_83", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["loinc_code"])
pipeline = Pipeline(stages=[\
documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,\
ner_radiology, ner_converter_radiology, ner_jsl, ner_converter_jsl,\
chunk_merger, loinc_mapper\
])
data = spark.createDataFrame([["The patient's glucose and hemoglobin A1c were checked, along with a basic metabolic panel including sodium and potassium, and a complete blood count."]]).toDF("text")
result = pipeline.fit(data).transform(data)
documentAssembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetectorDL = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = nlp.Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")\
.setInputCols(["sentence","token"])\
.setOutputCol("embeddings")
ner_radiology = medical.NerModel.pretrained("ner_radiology","en","clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner_radiology")
ner_converter_radiology = medical.NerConverterInternal()\
.setInputCols(["sentence","token","ner_radiology"])\
.setOutputCol("ner_chunk_radiology")\
.setWhiteList(["Test"])
ner_jsl = medical.NerModel.pretrained("ner_jsl","en","clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner_jsl")
ner_converter_jsl = medical.NerConverterInternal()\
.setInputCols(["sentence","token","ner_jsl"])\
.setOutputCol("ner_chunk_jsl")\
.setWhiteList(["Test"])
chunk_merger = medical.ChunkMergeApproach()\
.setInputCols("ner_chunk_jsl", "ner_chunk_radiology")\
.setOutputCol("ner_chunk")
loinc_mapper = medical.ChunkMapperModel.pretrained("loinc_numeric_mapper_2_83", "en", "clinical/models")\
.setInputCols(["ner_chunk"])\
.setOutputCol("mappings")\
.setRels(["loinc_code"])
pipeline = nlp.Pipeline(stages=[\
documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,\
ner_radiology, ner_converter_radiology, ner_jsl, ner_converter_jsl,\
chunk_merger, loinc_mapper\
])
data = spark.createDataFrame([["The patient's glucose and hemoglobin A1c were checked, along with a basic metabolic panel including sodium and potassium, and a complete blood count."]]).toDF("text")
result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentenceDetectorDL = SentenceDetectorDLModel
.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")
.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 ner_radiology = MedicalNerModel
.pretrained("ner_radiology", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner_radiology")
val ner_converter_radiology = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner_radiology"))
.setOutputCol("ner_chunk_radiology")
.setWhiteList(Array("Test"))
val ner_jsl = MedicalNerModel
.pretrained("ner_jsl", "en", "clinical/models")
.setInputCols(Array("sentence", "token", "embeddings"))
.setOutputCol("ner_jsl")
val ner_converter_jsl = new NerConverterInternal()
.setInputCols(Array("sentence", "token", "ner_jsl"))
.setOutputCol("ner_chunk_jsl")
.setWhiteList(Array("Test"))
val chunk_merger = new ChunkMergeApproach()
.setInputCols(Array("ner_chunk_jsl", "ner_chunk_radiology"))
.setOutputCol("ner_chunk")
val loincMapper = ChunkMapperModel.pretrained("loinc_numeric_mapper_2_83", "en", "clinical/models")
.setInputCols(Array("ner_chunk"))
.setOutputCol("mappings")
.setRels(Array("loinc_code"))
val pipeline = new Pipeline().setStages(Array(
documentAssembler, sentenceDetectorDL, tokenizer, word_embeddings,
ner_radiology, ner_converter_radiology, ner_jsl, ner_converter_jsl,
chunk_merger, loincMapper
))
val data = Seq("The patient's glucose and hemoglobin A1c were checked, along with a basic metabolic panel including sodium and potassium, and a complete blood count.").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
| ner_chunk | loinc_code | all_k_resolutions |
|:----------------------|:-------------|:----------------------------|
| glucose | 2345-7 | 2345-7::: |
| hemoglobin A1c | 41995-2 | 41995-2::: |
| basic metabolic panel | 51990-0 | 51990-0::: |
| sodium | 2951-2 | 2951-2::: |
| potassium | 2823-3 | 2823-3::: |
| complete blood count | 24317-0 | 24317-0:::24358-4:::24359-2 |
Model Information
| Model Name: | loinc_numeric_mapper_2_83 |
| Compatibility: | Healthcare NLP 6.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [ner_chunk] |
| Output Labels: | [mappings] |
| Language: | en |
| Size: | 12.8 MB |