Description
This model maps extracted clinical entities to ICD-10-CM codes using sbiobert_base_cased_mli sentence bert embeddings. In this model, synonyms having low cosine similarity to unnormalized terms are dropped. It returns the official resolution text within the brackets and also provides billable and Hierarchical Condition Categories (HCC) information of the codes in all_k_aux_labels parameter in the metadata. 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, billable status, hcc status, hcc score
Live Demo Open in Colab Copy S3 URICopied!
How to use
document_assembler = 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("word_embeddings")
ner = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")\
.setInputCols(["sentence", "token", "word_embeddings"])\
.setOutputCol("ner")\
ner_converter = NerConverterInternal()\
.setInputCols(["sentence", "token", "ner"])\
.setOutputCol("ner_chunk")\
.setWhiteList(["PROBLEM"])
c2doc = Chunk2Doc()\
.setInputCols("ner_chunk")\
.setOutputCol("ner_chunk_doc")
sbert_embedder = BertSentenceEmbeddings.pretrained("sbiobert_base_cased_mli", "en", "clinical/models")\
.setInputCols(["ner_chunk_doc"])\
.setOutputCol("sentence_embeddings")\
.setCaseSensitive(False)
icd_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_icd10cm_slim_billable_hcc", "en", "clinical/models") \
.setInputCols(["sentence_embeddings"]) \
.setOutputCol("resolution")\
.setDistanceFunction("EUCLIDEAN")
resolver_pipeline = Pipeline(stages = [document_assembler,
sentenceDetectorDL,
tokenizer,
word_embeddings,
ner,
ner_converter,
c2doc,
sbert_embedder,
icd_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, associated with obesity with a body mass index (BMI) of 33.5 kg/m2, presented with a one-week history of polyuria, polydipsia, and vomiting. Two weeks prior to presentation, she was treated with a five-day course of amoxicillin for a respiratory tract infection."""]]).toDF("text")
result = resolver_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], gestatio...|[O24.4, O24.41, Z86.32, O24.11, O24.81, P70.2, O24.01, O24.42, O24.414, ...|[0||0||0, 0||0||0, 1||0||0, 0||0||0, 0||0||0, 1||0||0, 0||0||0, 0||0||0,...|
|subsequent type two diabetes mellitus|PROBLEM| E11|[type 2 diabetes mellitus [type 2 diabetes mellitus], type 2 diabetes me...|[E11, E11.62, E11.5, E11.69, E11.59, E09, E11.6, E11.8, E11.4, E11.628, ...|[0||0||0, 0||0||0, 0||0||0, 1||1||18, 1||1||18, 0||0||0, 0||0||0, 1||1||...|
| obesity|PROBLEM| E66|[overweight and obesity [overweight and obesity], overweight [overweight...|[E66, E66.3, E66.8, E66.0, E66.1, E88.81, E66.09, E66.01, E34.4, E66.9, ...|[0||0||0, 1||0||0, 1||0||0, 0||0||0, 1||0||0, 1||0||0, 1||0||0, 1||1||22...|
| a body mass index|PROBLEM| Z68|[body mass index [bmi] [body mass index [bmi]], localized adiposity [loc...|[Z68, E65, L02.221, Z96.81, Y92.81, Y93.75, L02.23, L02.22, M67.49, R73,...|[0||0||0, 1||0||0, 1||0||0, 1||0||0, 0||0||0, 1||0||0, 0||0||0, 0||0||0,...|
| polyuria|PROBLEM| R35|[polyuria [polyuria], nocturnal polyuria [nocturnal polyuria], other pol...|[R35, R35.81, R35.89, R35.8, R31, R30.0, E72.01, R80, R34, R82.4, R82.99...|[0||0||0, 1||0||0, 1||0||0, 0||0||0, 0||0||0, 1||0||0, 1||1||23, 0||0||0...|
| polydipsia|PROBLEM| R63.1|[polydipsia [polydipsia], polyhydramnios [polyhydramnios], parasomnia [p...|[R63.1, O40, G47.5, R63.2, R00.2, G47.1, G47.13, F51.11, G47.19, L68.3, ...|[1||0||0, 0||0||0, 0||0||0, 1||0||0, 1||0||0, 0||0||0, 1||0||0, 1||0||0,...|
| vomiting|PROBLEM| R11.1|[vomiting [vomiting], cyclical vomiting [cyclical vomiting], nausea [nau...|[R11.1, G43.A, R11.0, R11, R11.14, R11.12, R23.1, G47.51, R11.10, H57.03...|[0||0||0, 0||0||0, 1||0||0, 0||0||0, 1||0||0, 1||0||0, 1||0||0, 1||0||0,...|
| a respiratory tract infection|PROBLEM| T17|[foreign body in respiratory tract [foreign body in respiratory tract], ...|[T17, T81.4, T81.81, J95.851, T17.8, Z87.0, J44.0, J06, T81.44, Z22, T17...|[0||0||0, 0||0||0, 0||0||0, 1||1||114, 0||0||0, 0||0||0, 1||1||111, 0||0...|
+-------------------------------------+-------+----------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+---------------------------------------------------------------------------+
Model Information
| Model Name: | sbiobertresolve_icd10cm_slim_billable_hcc |
| Compatibility: | Healthcare NLP 4.4.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [icd10cm_code] |
| Language: | en |
| Size: | 435.3 MB |
| Case sensitive: | false |