HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding

• URL: http://arxiv.org/abs/2212.04891v1
• Date: Fri, 9 Dec 2022 14:51:12 GMT
• Title: HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
• Authors: Shi Wang and Daniel Tang and Luchen Zhang and Huilin Li and Ding Han
• Abstract summary: International Classification of Diseases (ICD) is a set of classification codes for medical records. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes' hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction.
• Score: 2.9373912230684573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns unique International Classification of Diseases codes with each medical record, is widely used recently for its efficiency and error-prone avoidance. However, there are challenges that remain such as heterogeneity, label unbalance, and complex relationships between ICD codes. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes' hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction. We validate our method on two widely used datasets. Experimental results on two authoritative public datasets demonstrate that our proposed method boosts state-of-the-art performance by a large margin.

Related papers

• Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery [65.16724941038052]
  Category-aware Prototype Generation (CPG) and Discrimi Category 5.3% (DCE) are proposed. CPG enables the model to fully capture the intra-category diversity by representing each category with multiple prototypes. DCE boosts the discrimination ability of hash code with the guidance of the generated category prototypes.
  arXiv  Detail & Related papers  (2024-10-24T23:51:40Z)
• 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)
• A Novel ICD Coding Method Based on Associated and Hierarchical Code Description Distillation [6.524062529847299]
  ICD coding is a challenging multilabel text classification problem due to noisy medical document inputs. Recent advancements in automated ICD coding have enhanced performance by integrating additional data and knowledge bases with the encoding of medical notes and codes. We propose a novel framework based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code assignment.
  arXiv  Detail & Related papers  (2024-04-17T07:26:23Z)
• A Two-Stage Decoder for Efficient ICD Coding [10.634394331433322]
  We propose a two-stage decoding mechanism to predict ICD codes. At first, we predict the parent code and then predict the child code based on the previous prediction. Experiments on the public MIMIC-III data set show that our model performs well in single-model settings.
  arXiv  Detail & Related papers  (2023-05-27T17:25:13Z)
• Dynamic Conceptional Contrastive Learning for Generalized Category Discovery [76.82327473338734]
  Generalized category discovery (GCD) aims to automatically cluster partially labeled data. Unlabeled data contain instances that are not only from known categories of the labeled data but also from novel categories. One effective way for GCD is applying self-supervised learning to learn discriminate representation for unlabeled data. We propose a Dynamic Conceptional Contrastive Learning framework, which can effectively improve clustering accuracy.
  arXiv  Detail & Related papers  (2023-03-30T14:04:39Z)
• ICDBigBird: A Contextual Embedding Model for ICD Code Classification [71.58299917476195]
  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.
  arXiv  Detail & Related papers  (2022-04-21T20:59:56Z)
• Few-Shot Electronic Health Record Coding through Graph Contrastive Learning [64.8138823920883]
  We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph. CoGraph learns similarities and dissimilarities between HEWE graphs from different ICD codes so that information can be transferred among them. Two graph contrastive learning schemes, GSCL and GECL, exploit the HEWE graph structures so as to encode transferable features.
  arXiv  Detail & Related papers  (2021-06-29T14:53:17Z)
• 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)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• 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)

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.