Description
This model maps extracted medical entities to ICD-9-CM codes using sbiobert_base_cased_mli Sentence Bert Embeddings.
Predicted Entities
ICD-9-CM Codes
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_detector = 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")
clinical_ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")\
.setInputCols(["sentence","token","embeddings"])\
.setOutputCol("ner")
ner_converter = NerConverter()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(['PROBLEM'])
chunk2doc = Chunk2Doc().setInputCols("ner_chunk").setOutputCol("ner_chunk_doc")
sbert_embedder = BertSentenceEmbeddings\
.pretrained("sbiobert_base_cased_mli","en","clinical/models")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("sbert_embeddings")
icd9_resolver = SentenceEntityResolverModel\
.pretrained("sbiobertresolve_icd9","en", "clinical/models") \
.setInputCols(["ner_chunk", "sbert_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
nlpPipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter, chunk2doc, sbert_embedder, icd9_resolver])
clinical_note = [
'A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years '
'prior to presentation and subsequent type two diabetes mellitus, associated '
'with an acute hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, ']
data= spark.createDataFrame([clinical_note]).toDF('text')
results = nlpPipeline.fit(data).transform(data)
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 clinical_ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")
.setInputCols(Array("sentence","token","embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverter()
.setInputCols(Array("sentence","token","ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("PROBLEM"))
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")
val icd9_resolver = SentenceEntityResolverModel
.pretrained("sbiobertresolve_icd9","en", "clinical/models")
.setInputCols(Array("ner_chunk", "sbert_embeddings"))
.setOutputCol("resolution")
.setDistanceFunction("EUCLIDEAN")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, word_embeddings, clinical_ner, ner_converter, chunk2doc, sbert_embedder, icd9_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, associated with an acute hepatitis and obesity with a body mass index (BMI) of 33.5 kg/m2").toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.resolve.ic9").predict("""A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years """)
Results
+-------------------------------------+-------+---------+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+
| ner_chunk| entity|icd9_code| resolution| all_codes|
+-------------------------------------+-------+---------+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+
| gestational diabetes mellitus|PROBLEM| V12.21|[Personal history of gestational diabetes, Neonatal diabetes mellitus, Second...|[V12.21, 775.1, 249, 250, 249.7, 249.71, 249.9, 249.61, 648.0, 249.51, 249.11...|
|subsequent type two diabetes mellitus|PROBLEM| 249|[Secondary diabetes mellitus, Diabetes mellitus, Secondary diabetes mellitus ...|[249, 250, 249.9, 249.7, 775.1, 249.6, 249.8, V12.21, 249.71, V77.1, 249.5, 2...|
| an acute hepatitis|PROBLEM| 571.1|[Acute alcoholic hepatitis, Viral hepatitis, Autoimmune hepatitis, Injury to ...|[571.1, 070, 571.42, 902.22, 279.51, 571.4, 091.62, 572.2, 864, 070.0, 572.0,...|
| obesity|PROBLEM| 278.0|[Overweight and obesity, Morbid obesity, Overweight, Screening for obesity, O...|[278.0, 278.01, 278.02, V77.8, 278, 278.00, 272.2, 783.1, 277.7, 728.5, 521.5...|
| a body mass index|PROBLEM| V85|[Body mass index [BMI], Human bite, Localized adiposity, Effects of air press...|[V85, E928.3, 278.1, 993, E008.4, V61.5, 747.63, V85.5, 278.02, 780.97, 782.8...|
+-------------------------------------+-------+---------+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+
only showing top 5 rows
Model Information
| Model Name: | sbiobertresolve_icd9 |
| Compatibility: | Healthcare NLP 4.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [icd9_code] |
| Language: | en |
| Size: | 50.1 MB |
| Case sensitive: | false |