Description
This model maps clinical entities and concepts to ICD-10-CM codes using sbert_jsl_medium_uncased sentence bert embeddings and it supports 7-digit codes with Hierarchical Condition Categories (HCC) status. It also returns the official resolution text within the brackets inside the metadata. The model is augmented with synonyms, and previous augmentations are flexed according to cosine distances to unnormalized terms (ground truths).
In the result, look for the all_k_aux_labels parameter in the metadata to get HCC status. This column can be divided to get further details: billable status |
hcc status | hcc score. For example, if all_k_aux_labels is like 1||1||19 which means the billable status is 1, hcc status is 1, and hcc score is 19. |
Predicted Entities
ICD-10-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 = NerConverterInternal()\
.setInputCols(["sentence","token","ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["PROBLEM"])
chunk2doc = Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
bert_embeddings = BertSentenceEmbeddings.pretrained("sbert_jsl_medium_uncased", "en", "clinical/models")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("bert_embeddings")\
.setCaseSensitive(False)
icd10_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models")\
.setInputCols(["bert_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
nlpPipeline = Pipeline(stages=[document_assembler,
sentence_detector,
tokenizer,
word_embeddings,
clinical_ner,
ner_converter,
chunk2doc,
bert_embeddings,
icd10_resolver])
data_ner = 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, three years prior to presentation, associated with acute hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting."]]).toDF("text")
results = nlpPipeline.fit(data_ner).transform(data_ner)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val sentence_detector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare","en","clinical/models")
.setInputCols("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 clinical_ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")
.setInputCols(Array("sentence","token","embeddings"))
.setOutputCol("ner")
val ner_converter = new NerConverterInternal()
.setInputCols(Array("sentence","token","ner"))
.setOutputCol("ner_chunk")
.setWhiteList(Array("PROBLEM"))
val chunk2doc = new Chunk2Doc()
.setInputCols("ner_chunk")
.setOutputCol("ner_chunk_doc")
val bert_embeddings = BertSentenceEmbeddings.pretrained("sbert_jsl_medium_uncased", "en", "clinical/models")
.setInputCols("ner_chunk_doc")
.setOutputCol("bert_embeddings")
.setCaseSensitive(False)
val icd10_resolver = SentenceEntityResolverModel.pretrained("sbertresolve_icd10cm_augmented_billable_hcc", "en", "clinical/models")
.setInputCols("bert_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, icd10_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, three years prior to presentation, associated with acute hepatitis, and obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, poor appetite, and vomiting.").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+-------------------------------------+-------+----------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+
| ner_chunk| entity|icd10_code| resolutions| all_codes| hcc_list|
+-------------------------------------+-------+----------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+
| gestational diabetes mellitus|PROBLEM| O24.4|[gestational diabetes mellitus [gestational diabetes mellitus], maternal...| [O24.4, O24.41, O24.43, Z86.32, K86.8, P70.2, O24.434, E10.9, O24.430]|[0||0||0, 0||0||0, 0||0||0, 1||0||0, 0||0||0, 1||0||0, 1||0||0, 1||1||19...|
|subsequent type two diabetes mellitus|PROBLEM| E11|[type 2 diabetes mellitus [type 2 diabetes mellitus], type ii diabetes m...|[E11, E11.9, E10.9, E10, E13.9, Z83.3, L83, E11.8, E11.32, E10.8, Z86.39...|[0||0||0, 1||1||19, 1||1||19, 0||0||0, 1||1||19, 1||0||0, 1||0||0, 1||1|...|
| acute hepatitis|PROBLEM| K72.0|[acute hepatitis [acute and subacute hepatic failure], acute hepatitis a...|[K72.0, B15, B17.2, B17.1, B16, B17.9, B18.8, B15.9, K75.2, K73.9, B17.1...|[0||0||0, 0||0||0, 1||0||0, 0||0||0, 0||0||0, 1||0||0, 1||1||29, 1||0||0...|
| obesity|PROBLEM| E66.9|[obesity [obesity, unspecified], upper body obesity [other obesity], chi...| [E66.9, E66.8, P90, Q13.0, M79.4, Z86.39]| [1||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0]|
| a body mass index|PROBLEM| E66.9|[observation of body mass index [obesity, unspecified], finding of body ...|[E66.9, Z68.41, Z68, E66.8, Z68.45, Z68.4, Z68.1, Z68.2, R22.9, Z68.22, ...|[1||0||0, 1||1||22, 0||0||0, 1||0||0, 1||1||22, 0||0||0, 1||0||0, 0||0||...|
| polyuria|PROBLEM| R35|[polyuria [polyuria], sialuria [other specified metabolic disorders], st...|[R35, E88.8, R30.0, N28.89, O04.8, R82.4, E74.8, R82.2, E73.9, R82.0, R3...|[0||0||0, 0||0||0, 1||0||0, 1||0||0, 0||0||0, 1||0||0, 0||0||0, 1||0||0,...|
| polydipsia|PROBLEM| R63.1|[polydipsia [polydipsia], polyotia [accessory auricle], polysomia [conjo...|[R63.1, Q17.0, Q89.4, Q89.09, Q74.8, H53.8, H53.2, Q13.2, R63.8, E23.2, ...|[1||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0,...|
| poor appetite|PROBLEM| R63.0|[poor appetite [anorexia], excessive appetite [polyphagia], poor feeding...|[R63.0, R63.2, P92.9, R45.81, Z55.8, R41.84, R41.3, Z74.8, R46.89, R45.8...|[1||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0, 0||0||0, 1||0||0, 1||0||0,...|
| vomiting|PROBLEM| R11.1|[vomiting [vomiting], vomiting bile [vomiting following gastrointestinal...|[R11.1, K91.0, K92.0, A08.39, R11, P92.0, P92.09, R11.12, R11.10, O21.9,...|[0||0||0, 1||0||0, 1||0||0, 1||0||0, 0||0||0, 0||0||0, 1||0||0, 1||0||0,...|
+-------------------------------------+-------+----------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+
Model Information
| Model Name: | sbertresolve_icd10cm_augmented_billable_hcc |
| Compatibility: | Healthcare NLP 4.4.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [bert_embeddings] |
| Output Labels: | [icd10cm_code] |
| Language: | en |
| Size: | 938.6 MB |
| Case sensitive: | false |