Edit on GitHub
  • John Snow Labs
  • Jan 30, 2024

Pipeline for ICD-10-CM (General 3-character Codes)

  • licensed
  • en
  • clinical
  • icd10cm
  • pipeline
  • resolver

Description

This pipeline extracts the following entities and maps them to their ICD-10-CM codes using sbiobert_base_cased_mli sentence embeddings. It predicts ICD-10-CM codes up to 3 characters (according to ICD-10-CM code structure the first three characters represent the general type of injury or disease). Predicted Entities: COMMUNICABLE_DISEASE, DIABETES, DISEASE_SYNDROME_DISORDER, EKG_FINDINGS, HEART_DISEASE, HYPERLIPIDEMIA, HYPERTENSION, IMAGINGFINDINGS, INJURY_OR_POISONING, KIDNEY_DISEASE, OBESITY, ONCOLOGICAL, OVERWEIGHT, PREGNANCY, PSYCHOLOGICAL_CONDITION, SYMPTOM, VS_FINDING

Copy S3 URI

How to use


from sparknlp.pretrained import PretrainedPipeline

icd10cm_pipeline = PretrainedPipeline("icd10cm_generalised_resolver_pipeline", "en", "clinical/models")

text = """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, poor appetite, and vomiting. Two weeks prior to presentation, she was treated with a five-day course of amoxicillin for a respiratory tract infection."""

result = icd10cm_pipeline.fullAnnotate(text)



import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val icd10cm_pipeline = PretrainedPipeline("icd10cm_generalised_resolver_pipeline", "en", "clinical/models")

val text = """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, poor appetite, and vomiting. Two weeks prior to presentation, she was treated with a five-day course of amoxicillin for a respiratory tract infection."""

val result = icd10cm_pipeline.fullAnnotate(text)

Results


+-----------------------------+-----+---+----+-----------------------------------------------------------------+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+
|                       chunks|begin|end|code|                                                        all_codes|                                                                     resolutions|                                                                   all_distances|
+-----------------------------+-----+---+----+-----------------------------------------------------------------+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+
|gestational diabetes mellitus|   39| 67| O24|                                                  [O24, Z86, Z87]|[gestational diabetes mellitus [gestational diabetes mellitus], history of ge...|                                                        [0.0000, 0.0432, 0.0438]|
|   type two diabetes mellitus|  128|153| E11|                                             [E11, O24, E13, E10]|[type 2 diabetes mellitus [type 2 diabetes mellitus], pre-existing type 2 dia...|                                                [0.0065, 0.0374, 0.0388, 0.0394]|
|                      obesity|  172|178| E66|                         [E66, Z68, Q13, Z86, E34, H35, Z83, Q55]|[obesity [obesity, unspecified], obese [body mass index [bmi] 40.0-44.9, adul...|                [0.0000, 0.0264, 0.0278, 0.0403, 0.0414, 0.0449, 0.0473, 0.0482]|
|                     polyuria|  261|268| R35|[R35, E23, R31, R82, N40, E72, O04, R30, R80, E88, N03, P96, N02]|[polyuria [polyuria], polyuric state (disorder) [diabetes insipidus], hematur...|[0.0000, 0.1016, 0.1103, 0.1126, 0.1237, 0.1303, 0.1319, 0.1303, 0.1346, 0.13...|
|                   polydipsia|  271|280| R63|[R63, F63, E23, O40, G47, M79, R06, H53, I44, Q30, I45, R00, M35]|[polydipsia [polydipsia], psychogenic polydipsia [other impulse disorders], p...|[0.0000, 0.0455, 0.0635, 0.1474, 0.1520, 0.1553, 0.1641, 0.1675, 0.1648, 0.17...|
|                poor appetite|  283|295| R63|[R63, P92, R43, E86, R19, F52, Z72, R06, Z76, R53, R45, F50, R10]|[poor appetite [anorexia], poor feeding [feeding problem of newborn, unspecif...|[0.0000, 0.0603, 0.0730, 0.0837, 0.0844, 0.0847, 0.0861, 0.0870, 0.0871, 0.08...|
|                     vomiting|  302|309| R11|                                                  [R11, G43, P92]|[vomiting [vomiting], periodic vomiting [cyclical vomiting, in migraine, intr...|                                                        [0.0000, 0.0484, 0.0492]|
|  respiratory tract infection|  405|431| J98|               [J98, J06, A49, J04, J22, P28, Z59, T17, J20, J18]|[respiratory tract infection [other specified respiratory disorders], upper r...|[0.0000, 0.0472, 0.0524, 0.0698, 0.0706, 0.0803, 0.0798, 0.0789, 0.0885, 0.0911]|
+-----------------------------+-----+---+----+-----------------------------------------------------------------+--------------------------------------------------------------------------------+--------------------------------------------------------------------------------+

Model Information

Model Name: icd10cm_generalised_resolver_pipeline
Type: pipeline
Compatibility: Healthcare NLP 5.2.1+
License: Licensed
Edition: Official
Language: en
Size: 3.5 GB

Included Models

  • DocumentAssembler
  • SentenceDetectorDLModel
  • TokenizerModel
  • WordEmbeddingsModel
  • MedicalNerModel
  • NerConverter
  • Chunk2Doc
  • BertSentenceEmbeddings
  • SentenceEntityResolverModel