ICDBigBird: A Contextual Embedding Model for ICD Code Classification

• URL: http://arxiv.org/abs/2204.10408v1
• Date: Thu, 21 Apr 2022 20:59:56 GMT
• Title: ICDBigBird: A Contextual Embedding Model for ICD Code Classification
• Authors: George Michalopoulos, Michal Malyska, Nicola Sahar, Alexander Wong, Helen Chen
• Abstract summary: Contextual word embedding models have achieved state-of-the-art results in multiple NLP tasks. ICDBigBird is a BigBird-based model which can integrate a Graph Convolutional Network (GCN) Our experiments on a real-world clinical dataset demonstrate the effectiveness of our BigBird-based model on the ICD classification task.
• Score: 71.58299917476195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for healthcare reporting and management purposes. Assigning correct codes for clinical procedures is important for clinical, operational, and financial decision-making in healthcare. Contextual word embedding models have achieved state-of-the-art results in multiple NLP tasks. However, these models have yet to achieve state-of-the-art results in the ICD classification task since one of their main disadvantages is that they can only process documents that contain a small number of tokens which is rarely the case with real patient notes. In this paper, we introduce ICDBigBird a BigBird-based model which can integrate a Graph Convolutional Network (GCN), that takes advantage of the relations between ICD codes in order to create 'enriched' representations of their embeddings, with a BigBird contextual model that can process larger documents. Our experiments on a real-world clinical dataset demonstrate the effectiveness of our BigBird-based model on the ICD classification task as it outperforms the previous state-of-the-art models.

Related papers

• Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation [22.323705343864336]
  International Classification of Diseases (ICD) serves as a definitive medical classification system. The primary objective of ICD indexing is to allocate a subset of ICD codes to a medical record. Most existing approaches have suffered from selecting the proper label subsets from an extremely large ICD collection.
  arXiv  Detail & Related papers  (2024-05-29T13:54:30Z)
• Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification [22.323705343864336]
  We propose a novel approach for ICD indexing that adopts three ideas. We use a multi-level deep dilated residual convolution encoder to aggregate the information from the clinical notes. We formalize the task of ICD classification with auxiliary knowledge of the medical records.
  arXiv  Detail & Related papers  (2024-05-29T13:44:07Z)
• CoRelation: Boosting Automatic ICD Coding Through Contextualized Code Relation Learning [56.782963838838036]
  We propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations.
  arXiv  Detail & Related papers  (2024-02-24T03:25:28Z)
• Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse [53.797797404164946]
  The study highlights the difficulties faced in sharing tools and resources in this domain. We annotated a corpus of clinical documents according to 12 types of identifying entities. We build a hybrid system, merging the results of a deep learning model as well as manual rules.
  arXiv  Detail & Related papers  (2023-03-23T17:17:46Z)
• Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding [10.387366211090734]
  We propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database. Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions.
  arXiv  Detail & Related papers  (2022-04-22T14:12:22Z)
• Medical Code Prediction from Discharge Summary: Document to Sequence BERT using Sequence Attention [0.0]
  We propose a model based on bidirectional encoder representations from transformer (BERT) using the sequence attention method for automatic ICD code assignment. We evaluate our ap-proach on the MIMIC-III benchmark dataset.
  arXiv  Detail & Related papers  (2021-06-15T07:35:50Z)
• A Meta-embedding-based Ensemble Approach for ICD Coding Prediction [64.42386426730695]
  International Classification of Diseases (ICD) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information. Our proposed approach enhances the performance of neural models by effectively training word vectors using routine medical data as well as external knowledge from scientific articles.
  arXiv  Detail & Related papers  (2021-02-26T17:49:58Z)
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks [62.9447303059342]
  We show the importance of this problem in medical community. We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence. We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
  arXiv  Detail & Related papers  (2020-07-15T09:22:55Z)
• A Label Attention Model for ICD Coding from Clinical Text [14.910833190248319]
  We propose a new label attention model for automatic ICD coding. It can handle both the various lengths and the interdependence of the ICD code related text fragments. Our model achieves new state-of-the-art results on three benchmark MIMIC datasets.
  arXiv  Detail & Related papers  (2020-07-13T12:42:43Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.