Indexing ECG for Integrated Health Social Networks Predicting Keywords from ECG to Access Online Information
Huang, Yi, and Song, Insu (2024) Indexing ECG for Integrated Health Social Networks Predicting Keywords from ECG to Access Online Information. SN Computer Science, 5 (5). 596.
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Abstract
Health Social Networks (HSN) provide rich medical knowledge bases that are scalable and sustainable, while IoT provides non-invasive, pervasive, and low-cost methods to collect patient data. However, receiving relevant information from HSN is time consuming and challenging for users, such as searching for the right relevant information using keywords and filtering. On the other hand, healthcare IoT has limited access to the vast medical knowledge bases, such as HSN, to interpret the collected data. To address these challenges, we propose Keyword-based Integrated HSN of Things (KIHoT), an approach that combines the strengths of both HSNs and IoT to overcome their limitations. In this method, data (biosignals) collected via IoT devices are converted to human readable keywords using word embedding vector features and CNN (Convolutional Neural Network) predictors. The CNN predictors are trained to predict keywords that individuals search within an HSN to extract relevant information of the given biosignals. Those keywords are encoded as word embedding for searching relevant information. KIHoT utilizes contrast learning techniques to extract latent feature representations of electrocardiogram (ECG) signals, which are then used to predict disease-related keywords. The proposed method was evaluated using 11,936 ECG signals from patients with heart disease and achieved an accuracy of 98% for disease prediction. Our results suggest that KIHoT can effectively extract relevant information from HSN portals, making it easier for researchers and clinicians to access valuable medical knowledge.
| Item ID: | 86921 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2661-8907 |
| Keywords: | Electrocardiogram, Health social networks, Internet of things, Remote diagnosis, Word embedding |
| Copyright Information: | © The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
| Date Deposited: | 12 Nov 2025 05:01 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460102 Applications in health @ 100% |
| SEO Codes: | 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100% |
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