Sentence Entity Resolver for Logical Observation Identifiers Names and Codes (LOINC) codes (sbluebert_base_uncased_mli embeddings)

val document_assembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("document")

val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
  .setInputCols(Array("document"))
  .setOutputCol("sentence")

val tokenizer = new Tokenizer()
  .setInputCols(Array("sentence"))
  .setOutputCol("token")

val word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical","en","clinical/models")
  .setInputCols(Array("sentence","token"))
  .setOutputCol("embeddings")

val ner_model = MedicalNerModel.pretrained("ner_radiology", "en", "clinical/models")
 	.setInputCols(Array("sentence", "token", "embeddings"))
 	.setOutputCol("ner_radiology")

val ner_converter = new NerConverterInternal()
  	  .setInputCols(Array("sentence", "token", "ner_radiology"))
 	  .setOutputCol("ner_chunk_radiology")
	  .setWhiteList(Array("Test"))

val ner_model_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("merged_ner_chunk")

val chunk2doc = new Chunk2Doc()
  	.setInputCols("merged_ner_chunk")
  	.setOutputCol("ner_chunk_doc")

val sbert_embedder = BertSentenceEmbeddings.pretrained("sbluebert_base_uncased_mli", "en", "clinical/models")
		.setInputCols("ner_chunk_doc")
		.setOutputCol("sbluebert_embeddings")
		.setCaseSensitive(false)

val resolver = SentenceEntityResolverModel.pretrained("sbluebertresolve_loinc_uncased","en", "clinical/models")
	.setInputCols("sbluebert_embeddings")
	.setOutputCol("resolution")
	.setDistanceFunction("EUCLIDEAN")


val nlpPipeline = new Pipeline().setStages(Array(document_assembler,
                               sentence_detector,
                               tokenizer,
                               word_embeddings,
                               ner_model,
                               ner_converter,
							   ner_model_jsl,
 							   ner_converter_jsl,
							   chunk_merger,
                               chunk2doc,
                               sbert_embedder,
                               resolver))

val data = Seq("""A 65-year-old woman presents to the office with generalized fatigue for the last 4 months.
  She used to walk 1 mile each evening but now gets tired after 1-2 blocks. She has a history of Crohn disease and hypertension
  for which she receives appropriate medications. She is married and lives with her husband. She eats a balanced diet that
  includes chicken, fish, pork, fruits, and vegetables. She rarely drinks alcohol and denies tobacco use. A physical examination
  is unremarkable. Laboratory studies show the following: Hemoglobin: 9.8g/dL, Hematocrit: 32%, Mean Corpuscular Volume: 110 μm3""").toDF("text")

val result = nlpPipeline.fit(data).transform(data)