ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network (1912.00862v1)

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.

• The paper presents MultiResCNN, a model that improves ICD coding by integrating multi-filter convolutional layers with residual connections.
• It addresses challenges of varied clinical text patterns and outperforms existing methods on MIMIC datasets using key performance metrics.
• The study shows that enhanced feature representation significantly boosts coding accuracy despite higher computational demands.

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

The paper "ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network" by Fei Li and Hong Yu presents a novel approach to automated ICD coding from clinical texts. The proposed method, termed MultiResCNN, leverages deep learning techniques to improve the performance and adaptability of automated coding systems by exploiting the strengths of convolutional neural networks (CNNs) and residual networks.

The paper begins with a clear identification of the challenges associated with varying text lengths and structures, which hinder the performance of conventional CNN architectures in capturing diverse clinical language patterns necessary for accurate ICD coding. To address these limitations, the authors introduce a multi-filter convolutional layer within their neural network architecture that employs filters of varying lengths to better capture diverse text patterns. They complement this with a residual convolutional layer to expand the receptive field and deepen the architecture without degrading performance, thereby enhancing feature representation capacity.

Evaluations conducted on the MIMIC datasets—specifically MIMIC-III's full code set and top-50 code set, as well as MIMIC-II's full code set—demonstrate impressive performance improvements over existing state-of-the-art models. With emphases on critical metrics such as macro- and micro-F1, macro- and micro-AUC, and precision at K, the MultiResCNN model shows significant advancements, particularly in MIMIC-III's top-50 code task where it outperformed baselines across all metrics.

These findings suggest that deep, diversified text representations significantly enhance ICD coding tasks, offering a promising direction for further research. The enhancements in capturing varied linguistic structures point to the importance of model flexibility and depth in text classification tasks, reinforcing the role of advanced neural architectures in healthcare informatics.

While the MultiResCNN introduces increased computational demands, the improvements in predictive accuracy present a compelling trade-off. Practical applications of this model could streamline billing processes, reduce manual coding errors, and contribute to more efficient healthcare delivery.

Looking forward, further research might explore the integration of contextual LLMs like BERT, although initial results indicated challenges due to processing limitations and input constraints. The paper emphasizes a promising path for ongoing improvements in text classification methodologies through deep learning innovations.

The results are notable considering the competitive nature of automated ICD coding tasks, and the emphasis on both theoretical contributions and practical utility marks this paper as a significant work in AI-driven healthcare solutions.