Sentence Entity Resolver for UMLS Codes - General Concepts

Description

This model maps clinical entities and concepts to the following 4 UMLS CUI code categories using ´mpnet_embeddings_biolord_2023_c´ Sentence Embeddings:

Disease: Unique Identifier: T047 Tree Number: B2.2.1.2.1

Symptom: Unique Identifier: T184 Tree Number: A2.2.2

Medication: Unique Identifier: T074 Tree Number: A1.3.1

Procedure: Unique Identifier: T061 Tree Number: B1.3.1.3

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How to use

documentAssembler = DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

sentenceDetector = 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_model = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models")\
    .setInputCols(["sentence", "token", "embeddings"])\
    .setOutputCol("ner_jsl")

ner_model_converter = NerConverterInternal()\
    .setInputCols(["sentence", "token", "ner_jsl"])\
    .setOutputCol("ner_chunk")\
    .setWhiteList(["Injury_or_Poisoning","Hyperlipidemia","Kidney_Disease","Oncological","Cerebrovascular_Disease",
                  "Oxygen_Therapy","Heart_Disease","Obesity","Disease_Syndrome_Disorder","Symptom","Treatment","Diabetes","Injury_or_Poisoning",
                  "Procedure","Symptom","Treatment","Drug_Ingredient","VS_Finding","Communicable_Disease",
                  "Drug_BrandName","Hypertension"])

chunk2doc = Chunk2Doc()\
    .setInputCols("ner_chunk")\
    .setOutputCol("ner_chunk_doc")

embeddings =MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c","en")\
    .setInputCols(["ner_chunk_doc"])\
    .setOutputCol("mpnet_embeddings")\
    .setCaseSensitive(False)

umls_resolver = SentenceEntityResolverModel.pretrained("biolordresolve_umls_general_concepts", "en", "clinical/models")\
     .setInputCols(["mpnet_embeddings"])\
     .setOutputCol("resolution")\
    .setDistanceFunction("EUCLIDEAN")\
    .setCaseSensitive(False)

resolver_pipeline = Pipeline(stages=[
    documentAssembler,
    sentenceDetector,
    tokenizer,
    word_embeddings,
    ner_model,
    ner_model_converter,
    chunk2doc,
    embeddings,
    umls_resolver
])

data = spark.createDataFrame([["""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior episode of pancreatitis three years prior to presentation, associated with an acute hepatitis, and obesity with a BMI of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting."""]]).toDF("text")

result = resolver_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 ner_model = MedicalNerModel
      .pretrained("ner_jsl", "en", "clinical/models")
      .setInputCols(Array("sentence", "token", "embeddings"))
      .setOutputCol("ner_jsl")

val ner_model_converter = new NerConverterInternal()
      .setInputCols(Array("sentence", "token", "ner_jsl"))
      .setOutputCol("ner_chunk")
      .setWhiteList(["Injury_or_Poisoning","Hyperlipidemia","Kidney_Disease","Oncological","Cerebrovascular_Disease",
                    "Oxygen_Therapy", "Heart_Disease","Obesity","Disease_Syndrome_Disorder","Symptom","Treatment","Diabetes","Injury_or_Poisoning",
                    "Procedure","Symptom","Treatment","Drug_Ingredient","VS_Finding","Communicable_Disease",
                    "Drug_BrandName","Hypertension","Imaging_Technique"
                  ])

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

val sbert_embedder = BertSentenceEmbeddings
      .pretrained("sbiobert_base_cased_mli", "en","clinical/models")
      .setInputCols(Array("ner_chunk_doc"))
      .setOutputCol("sbert_embeddings")
      .setCaseSensitive(False)

val resolver = SentenceEntityResolverModel
      .pretrained("sbiobertresolve_umls_findings", "en", "clinical/models")
      .setInputCols(Array("ner_chunk_doc", "sbert_embeddings"))
      .setOutputCol("resolution")
      .setDistanceFunction("EUCLIDEAN")

val embeddings =MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023_c","en")
    .setInputCols(Array("ner_chunk_doc"))
    .setOutputCol("mpnet_embeddings")
    .setCaseSensitive(False)

val umls_resolver = SentenceEntityResolverModel.pretrained("biolordresolve_umls_general_concepts", "en", "clinical/models")
    .setInputCols(["mpnet_embeddings"])
    .setOutputCol("resolution")
    .setDistanceFunction("EUCLIDEAN")
    .setCaseSensitive(False)

val resolver_pipeline = new Pipeline().setStages(Array(
    document_assembler,
    sentence_detector,
    tokenizer,
    word_embeddings,
    ner_model,
    ner_model_converter,
    chunk2doc,
    embeddings,
    umls_resolver))

val data = Seq([["""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus (T2DM), one prior episode of pancreatitis three years prior to presentation, associated with an acute hepatitis, and obesity with a BMI of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting."""]]).toDF("text")

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

Results

+-----------------------------+-----+---+-------------------------+---------+---------------------------------+------------------------------------------------------------+------------------------------------------------------------+
|                    ner_chunk|begin|end|                   entity|umls_code|                      description|                                               all_k_results|                                           all_k_resolutions|
+-----------------------------+-----+---+-------------------------+---------+---------------------------------+------------------------------------------------------------+------------------------------------------------------------+
|gestational diabetes mellitus|   39| 67|                 Diabetes| C0085207|Gestational diabetes mellitus NOS|C0085207:::C0032969:::C0341893:::C0341898:::C1391475:::C2...|Gestational diabetes mellitus NOS:::Unspecified diabetes ...|
|   type two diabetes mellitus|  128|153|                 Diabetes| C0011860|         Type 2 Diabetes Mellitus|C0011860:::C1719939:::C0877302:::C1832544:::C0342262:::C1...|Type 2 Diabetes Mellitus:::Disorder associated with type ...|
|                         T2DM|  156|159|                 Diabetes| C0011860|                              T2D|C0011860:::C1832387:::C1835887:::C4015183:::C1832544:::C1...|T2D:::T2D2:::TNDM2:::T2D5:::T2D1:::T2D4:::TPMTD:::T2D3:::...|
|                 pancreatitis|  184|195|Disease_Syndrome_Disorder| C0030305|                     pancreatitis|C0030305:::C0856100:::C0267948:::C0267946:::C0001339:::C1...|pancreatitis:::Pancreatitis aggravated:::Metabolic pancre...|
|                    hepatitis|  257|265|Disease_Syndrome_Disorder| C0019158|                        Hepatitis|C0019158:::C0019159:::C0040860:::C0744855:::C0854496:::C0...|Hepatitis:::a hepatitis:::Portal hepatitis:::hepatitis im...|
|                      obesity|  272|278|                  Obesity| C0028754|                          OBESITY|C0028754:::C0451819:::C1561826:::C0149974:::C0028756:::C2...|OBESITY:::Simple obesity:::Overweight and obesity:::PRIMA...|
|                     polyuria|  343|350|                  Symptom| C0032617|                         Polyuria|C0032617:::C2830339:::C3888890:::C0016708:::C0848232:::C3...|Polyuria:::Other polyuria:::Polyuria-polydipsia syndrome:...|
|                   polydipsia|  353|362|                  Symptom| C0085602|                   Polydipsia NOS|C0085602:::C3888890:::C1540939:::C0857397:::C0241379:::C1...|Polydipsia NOS:::Polyuria-polydipsia syndrome:::(Excessiv...|
|                poor appetite|  365|377|                  Symptom| C0232462|          Decreased food appetite|        C0232462:::C0003123:::C1971623:::C0426583:::C0426579|Decreased food appetite:::APPETITE IMPAIRED:::Appetite lo...|
|                     vomiting|  384|391|                  Symptom| C0042963|                    Vomiting, NOS|C0042963:::C0027498:::C3825500:::C3874311:::C0474496:::C0...|Vomiting, NOS:::nausea with vomiting:::Vomiting in childr...|
+-----------------------------+-----+---+-------------------------+---------+---------------------------------+------------------------------------------------------------+------------------------------------------------------------+

Model Information

Model Name:	biolordresolve_umls_general_concepts
Compatibility:	Healthcare NLP 5.3.1+
License:	Licensed
Edition:	Official
Input Labels:	[mpnet_embeddings]
Output Labels:	[umls_code]
Language:	en
Size:	3.9 GB
Case sensitive:	false

Dependency

This model can be used with spark v3.4.0 and above versions.

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