Pipeline for Hierarchical Condition Categories (HCC) Sentence Entity Resolver

Description

This advanced pipeline extracts clinical conditions from clinical texts and utilizes the sbiobert_base_cased_mli Sentence Bert Embeddings to map these entities to their corresponding Hierarchical Condition Categories (HCC) codes.

Predicted Entities

PROBLEM

Open in Colab Copy S3 URI

How to use


from sparknlp.pretrained import PretrainedPipeline

ner_pipeline = PretrainedPipeline("hcc_resolver_pipeline", "en", "clinical/models")

result = ner_pipeline.annotate("""The patient's medical record indicates a diagnosis of Diabetes and Chronic Obstructive Pulmonary Disease, requiring comprehensive care and management.""")


import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline

val ner_pipeline = PretrainedPipeline("hcc_resolver_pipeline", "en", "clinical/models")

val result = ner_pipeline.annotate("""The patient's medical record indicates a diagnosis of Diabetes and Chronic Obstructive Pulmonary Disease, requiring comprehensive care and management.""")

Results

|    | chunks                                |   begin |   end | entities   |   hcc_code | resolutions                                                                                                                                                                                                                                                                                                                                 | all_codes          |
|---:|:--------------------------------------|--------:|------:|:-----------|-----------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------|
|  0 | Diabetes                              |      54 |    61 | PROBLEM    |         19 | diabetes monitored [type 2 diabetes mellitus without complications]:::anaemia of diabetes [anemia, unspecified]:::anemia of diabetes (disorder) [type 2 diabetes mellitus with other specified complication]                                                                                                                                | 19:::0:::18        |
|  1 | Chronic Obstructive Pulmonary Disease |      67 |   103 | PROBLEM    |        111 | chronic obstructive pulmonary disease [chronic obstructive pulmonary disease, unspecified]:::chronic lung disease [pneumoconiosis due to other dust containing silica]:::chronic pulmonary heart disease [pulmonary heart disease, unspecified]:::other chronic obstructive pulmonary disease [other chronic obstructive pulmonary disease] | 111:::112:::85:::0 |

Model Information

Model Name: hcc_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
  • NerConverterInternalModel
  • Chunk2Doc
  • BertSentenceEmbeddings
  • SentenceEntityResolverModel