Automatically assigning multiple international Classification of Diseases (ICD) codes to a clinical note is challenging due to the unstructured and verbose nature of medical records. Currently, most methods employ dee...
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ISBN:
(纸本)9789819794393;9789819794409
Automatically assigning multiple international Classification of Diseases (ICD) codes to a clinical note is challenging due to the unstructured and verbose nature of medical records. Currently, most methods employ deep neural networks to learn the representation of clinical notes from a single perspective. These single-view-based methods overlook the exploitation and fusion of multiview features to enhance the precision of ICD coding. In this paper, we propose a new Multiview Attention Network (MANet) to extract and fuse multiview features for ICD coding. MANet includes a specially designed multiview attention scheme to extract and fuse coarse, global, and local features from clinical notes. Additionally, a novel cascaded multilayer perceptron (MLP) block and a multiscale convolution block are designed to extract global and local features, respectively. Self-attention and cross-attention are integrated to effectively fuse these multiview features, generating more informative and discriminative representations. Extensive experiments conducted on the popular MIMIC-III and MIMIC-IV-ICD9 datasets demonstrate the superiority of our proposed MANet over state-of-the-art methods. On MIMIC-III, MANet achieves a Macro-AUC of 0.953, Micro-AUC of 0.993, Macro-F1 of 0.135, Micro-F1 of 0.596, precision at top 8 (P@8) of 0.773, and precision at top 15 (P@15) of 0.617. On MIMIC-IV-ICD9, MANet achieves a Macro-AUC of 0.968, Micro-AUC of 0.996, Macro-F1 of 0.146, Micro-F1 of 0.614, P@8 of 0.698, and P@15 of 0.526.
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The tobacco industry faces numerous network security challenges during its digital transformation. With the picture of Chinese informationtechnology application innovation (ITAI), network security issues have become ...
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The tobacco industry faces numerous network security challenges during its digital transformation. With the picture of Chinese informationtechnology application innovation (ITAI), network security issues have become more prominent. This article explores to evaluate network security in the tobacco industry's digital environment by verification and testing. First, it proposes a security index system for ITAI products. Second, it conducts formal verification using the ProVerif to detect vulnerabilities and security issues in the network system in the ITAI environment. Thirdly, a combination testing method is designed to assess the security of ITAI hosts and security defense products. Through formal verification and testing, this research provides theoretical support and practical guidance for network security protection in the tobacco industry's digital environment.
Most of the content on various social media platforms has enormous textual data. Before being used in machine learning models, this textual data must be transformed into numerical formats using feature extraction and ...
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