Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding

• URL: http://arxiv.org/abs/2204.10716v1
• Date: Fri, 22 Apr 2022 14:12:22 GMT
• Title: Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding
• Authors: Leibo Liu, Oscar Perez-Concha, Anthony Nguyen, Vicki Bennett, Louisa Jorm
• Abstract summary: 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.
• Score: 10.387366211090734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes a pretrained Transformer model to represent the tokens of clinical documents. We subsequently employ a two-level hierarchical label-wise attention mechanism that creates label-specific document representations. These representations are in turn used by a feed-forward neural network to predict whether a specific ICD code is assigned to the input clinical document of interest. We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database. To investigate the performance of different types of Transformer models, we develop ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using all the MIMIC-III clinical notes. The experiment results show that the F1 scores of the HiLAT+ClinicalplusXLNet outperform the previous state-of-the-art models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions.

Related papers

• Improving ICD coding using Chapter based Named Entities and Attentional Models [0.0]
  We introduce an enhanced approach to ICD coding that improves F1 scores by using chapter-based named entities and attentional models. This method categorizes discharge summaries into ICD-9 Chapters and develops attentional models with chapter-specific data. For categorization, we use Chapter-IV to de-bias and influence key entities and weights without neural networks.
  arXiv  Detail & Related papers  (2024-07-24T12:34:23Z)
• Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study [60.56194508762205]
  We reproduce, compare, and analyze state-of-the-art automated medical coding machine learning models. We show that several models underperform due to weak configurations, poorly sampled train-test splits, and insufficient evaluation. We present the first comprehensive results on the newly released MIMIC-IV dataset using the reproduced models.
  arXiv  Detail & Related papers  (2023-04-21T11:54:44Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt [7.554528566861559]
  This study transforms this multi-label classification task into an autoregressive generation task. Instead of directly predicting the high dimensional space of ICD codes, our model generates the lower dimension of text descriptions. Experiments on MIMIC-III-few show that our model performs with a marco F1 30.2, which substantially outperforms the previous MIMIC-III-full SOTA model.
  arXiv  Detail & Related papers  (2022-11-24T22:10:50Z)
• Cross-modal Clinical Graph Transformer for Ophthalmic Report Generation [116.87918100031153]
  We propose a Cross-modal clinical Graph Transformer (CGT) for ophthalmic report generation (ORG) CGT injects clinical relation triples into the visual features as prior knowledge to drive the decoding procedure. Experiments on the large-scale FFA-IR benchmark demonstrate that the proposed CGT is able to outperform previous benchmark methods.
  arXiv  Detail & Related papers  (2022-06-04T13:16:30Z)
• 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)
• CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification [70.554573538777]
  We argue for hierarchical evaluation of the predictions of neural LMTC models. We describe a structural issue in the representation of the structured label space in prior art. We propose a set of metrics for hierarchical evaluation using the depth-based representation.
  arXiv  Detail & Related papers  (2021-09-10T13:09:12Z)
• 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)
• TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding [5.273190477622007]
  International Classification of Disease (ICD) coding procedure has been shown to be effective and crucial to the billing system in medical sector. Currently, ICD codes are assigned to a clinical note manually which is likely to cause many errors. In this project, we apply a transformer-based architecture to capture the interdependence among the tokens of a document and then use a code-wise attention mechanism to learn code-specific representations of the entire document.
  arXiv  Detail & Related papers  (2021-03-28T05:34:32Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• An Explainable CNN Approach for Medical Codes Prediction from Clinical Text [1.7746314978241657]
  We develop CNN-based methods for automatic ICD coding based on clinical text from intensive care unit (ICU) stays. We come up with the Shallow and Wide Attention convolutional Mechanism (SWAM), which allows our model to learn local and low-level features for each label.
  arXiv  Detail & Related papers  (2021-01-14T02:05:34Z)

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.