Improving ICD coding using Chapter based Named Entities and Attentional Models
<|reference_start|>Improving ICD coding using Chapter based Named Entities and Attentional Models: Recent advancements in natural language processing (NLP) have led to automation in various domains. However, clinical NLP often relies on benchmark datasets that may not reflect real-world scenarios accurately. Automatic ICD coding, a vital NLP task, typically uses outdated and imbalanced datasets like MIMIC-III, with existing methods yielding micro-averaged F1 scores between 0.4 and 0.7 due to many false positives. Our research introduces 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, eliminating the need to consider external data for code identification. For categorization, we use Chapter-IV to de-bias and influence key entities and weights without neural networks, creating accurate thresholds and providing interpretability for human validation. Post-validation, we develop attentional models for three frequent and three non-frequent codes from Chapter-IV using Bidirectional-Gated Recurrent Units (GRUs) with Attention and Transformer with Multi-head Attention architectures. The average Micro-F1 scores of 0.79 and 0.81 from these models demonstrate significant performance improvements in ICD coding.<|reference_end|>
@article{beeravolu2024improving,
    title={Improving ICD coding using Chapter based Named Entities and Attentional
  Models},
    author={Abhijith R. Beeravolu, Mirjam Jonkman, Sami Azam, Friso De Boer},
    journal={arXiv preprint arXiv:2407.17230},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17230},
    primaryClass={cs.CL cs.AI}
}