ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

2019/11/25 by Fei Li, Hong Yu, Li, Fei +1 · 6 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1912.00862

openalex publication_date 2019/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field. We evaluated the effectiveness of our model on the widely-used MIMIC dataset. On the full code set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of 6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set of MIMIC-II, our model outperformed all the existing and state-of-the-art models in all evaluation metrics. The code is available at https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network.

Citations

• Adam: A Method for Stochastic Optimization
• Learning to Diagnose with LSTM Recurrent Neural Networks
• ImageNet classification with deep convolutional neural networks

Cited by

• MedDCR: Learning to Design Agentic Workflows for Medical Coding
• TACL: Threshold-Adaptive Curriculum Learning Strategy for Enhancing Medical Text Understanding
• LTR-ICD: A Learning-to-Rank Approach for Automatic ICD Coding
• Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs
• Code Like Humans: A Multi-Agent Solution for Medical Coding
• Federated Learning for ICD Classification with Lightweight Models and Pretrained Embeddings

Related

• TransICD: Transformer Based Code-wise Attention Model for Explainable ICD Coding
• An Explainable CNN Approach for Medical Codes Prediction from Clinical Text
• Multimodal Machine Learning for Automated ICD Coding
• An Automatic ICD Coding Network Using Partition-Based Label Attention
• A Two-Stage Decoder for Efficient ICD Coding