IEEE Transactions on Computational Social Systems最新文献_第8页

Enhanced Knowledge Tracing With Learnable Filter 利用可学习过滤器增强知识跟踪

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-01 DOI: 10.1109/TCSS.2024.3452130

Fulan Qian;Yetong Hu;Guangyao Li;Jie Chen;Shijin Wang;Shu Zhao

{"title":"Enhanced Knowledge Tracing With Learnable Filter","authors":"Fulan Qian;Yetong Hu;Guangyao Li;Jie Chen;Shijin Wang;Shu Zhao","doi":"10.1109/TCSS.2024.3452130","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3452130","url":null,"abstract":"The primary objective of knowledge tracing (KT) is to evaluate students’ understanding and mastery of knowledge through their responses to exercises, which aids in predicting their future performance. Deep neural networks have been widely applied in the area of knowledge tracing and have demonstrated encouraging results. Nevertheless, in real-world scenarios, there is a substantial amount of noise in students’ response records. These noises may amplify the inherent risk of overfitting in deep neural networks, leading to a decrease in model performance. To address these issues, we introduce a new model called filter knowledge tracing (FKT). This innovative model incorporates a learnable filter into KT to filter out noise information from students’ exercise sequences. We redefine the input paradigm of the data, using learnable filters to perform filtering operations in its frequency domain representation space, effectively removing noise. Additionally, an attention module has been introduced in the FKT model to evaluate the impact of students’ historical interactions on their current knowledge state. To validate our model, we conduct extensive experiments utilizing four publicly available datasets. The results demonstrate that FKT outperforms existing benchmarks, particularly on larger datasets, signifying an improvement in KT performance while effectively reducing the risk of overfitting.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"198-209"},"PeriodicalIF":4.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information Disorder Amidst Crisis: A Case Study of COVID-19 in India 危机中的信息混乱：以印度新冠肺炎疫情为例

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-10-01 DOI: 10.1109/TCSS.2024.3450788

Mohammad Affan;Syed Shafat Ali;Tarique Anwar;Ajay Rastogi

{"title":"Information Disorder Amidst Crisis: A Case Study of COVID-19 in India","authors":"Mohammad Affan;Syed Shafat Ali;Tarique Anwar;Ajay Rastogi","doi":"10.1109/TCSS.2024.3450788","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3450788","url":null,"abstract":"The devastation led by the COVID-19 pandemic was accompanied by a plethora of misinformation, laden with pseudoscience, hoaxes, and myths, often intertwined with hate speech. This phenomenon was particularly pronounced in India, where the intricate political and communal landscape provided fertile ground. The misinformation, with its elements of hate speech, posed a significant threat to societal cohesion. In response, this article delves into the dynamics of misinformation during the COVID-19 crisis in India, with a specific focus on differentiating general misinformation (GM) from hateful misinformation (HM). To this end, we construct an Indian COVID-19 misinformation dataset collected from various online social and mainstream media and analyze it from various perspectives. Mainly, we focus on temporal evolution, content and topics involved, and emotions and sentiment sensationalism of COVID-19 misinformation. We found the emotions of sadness and fear as key amplifiers of misinformation in general, with negative sentiments dominating HM. Through our comprehensive analysis, we found many such interesting insights and patterns. We also perform hate detection within misinformation content using various unsupervised and supervised learning techniques. Our results show that while GM is relatively easier to identify, it is challenging to detect HM. Overall, deep learning models are found to be more effective than unsupervised methods. By discovering key insights and patterns, this study serves as a foundation for developing robust strategies to combat information disorder.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"175-184"},"PeriodicalIF":4.5,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph Anomaly Detection via Multiscale Contrastive Self-Supervised Learning From Local to Global 基于局部到全局多尺度对比自监督学习的图异常检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-30 DOI: 10.1109/TCSS.2024.3457161

Xiaofeng Wang;Shuaiming Lai;Shuailei Zhu;Yuntao Chen;Laishui Lv;Yuanyuan Qi

{"title":"Graph Anomaly Detection via Multiscale Contrastive Self-Supervised Learning From Local to Global","authors":"Xiaofeng Wang;Shuaiming Lai;Shuailei Zhu;Yuntao Chen;Laishui Lv;Yuanyuan Qi","doi":"10.1109/TCSS.2024.3457161","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3457161","url":null,"abstract":"Graph anomaly detection is a challenging task in graph data mining, aiming to recognize unconventional patterns within a network. Recently, there has been increasing attention on graph anomaly detection based on contrastive learning due to its high adaptability to the sample imbalance problem. However, most existing work typically focuses on the contrast of local views while neglecting global comparison information, leading to suboptimal performance. To address this issue, we introduce a new multiscale contrastive self-supervised learning framework for graph anomaly detection (GADMCLG). Our approach incorporates local-level contrasts involving node–node and node–subgraph contrast, and global-level subgraph–subgraph contrast. The former mines localized abnormal information, while the latter is intended to capture global anomalous patterns. Specifically, our proposed subgraph–subgraph contrast adopts the <italic>h-order neighbor subgraph sampling instead of augmented subgraphs through edge perturbation. This sampling strategy ensures a comprehensive observation of the neighborhood surrounding the target node, thereby mitigating the introduction of extraneous noise and providing interpretability for the detected results. Furthermore, we incorporate a subgraph centralization technique to reduce the bias caused by the absolute position of subgraphs in the attribute space, which enhances the model's ability to identify anomalies at different scales. Extensive experimental results on six real-world datasets demonstrate the effectiveness of our method and its superiority compared with state-of-the-art approaches.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"485-497"},"PeriodicalIF":4.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized Product Description Generation With Gated Pointer-Generator Transformer 个性化产品描述生成与门控指针发电机变压器

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-27 DOI: 10.1109/TCSS.2024.3396840

Yu-Sen Liang;Chih-Yao Chen;Cheng-Te Li;Sheng-Mao Chang

{"title":"Personalized Product Description Generation With Gated Pointer-Generator Transformer","authors":"Yu-Sen Liang;Chih-Yao Chen;Cheng-Te Li;Sheng-Mao Chang","doi":"10.1109/TCSS.2024.3396840","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3396840","url":null,"abstract":"In the realm of e-commerce, where online shopping has become a staple of daily life, the generation of personalized product descriptions presents a unique challenge and opportunity for enhancing customer experience. Traditional retail interactions allow for personalized communication between salespersons and customers, ensuring that consumer needs are directly addressed. This level of personalization is harder to achieve online, where customers must navigate through generic, often lengthy product descriptions to make informed purchasing decisions. Recognizing the dual necessity of personalizing content to individual preferences while ensuring the descriptions remain faithful to the product's core attributes, this article introduces a novel approach, the gated pointer-generator transformer (GPGT). This framework is designed to bridge the gap between customer preferences and product features, enabling the generation of descriptions that are not only customized to the user's interests—such as emphasizing appearance for fashion-forward individuals or functionality for tech enthusiasts—but also accurately reflect the product's distinctive qualities, including brand names and technical specifications. GPGT leverages the select-attention mechanism combined with a Transformer encoder to capture the nuanced interactions between user attributes and product features, further refined by a copy mechanism during the decoding phase for the precise inclusion of specific product-related terms. Extensive experiments show that our framework substantially improves the quality of generation (<inline-formula><tex-math>$+$</tex-math></inline-formula>10.6% on ROUGE-2 and <inline-formula><tex-math>$+$</tex-math></inline-formula>15.9% on BLEU) while being more faithful to draw people's attention. The results on human evaluation, in terms of fluency, faithfulness, and personalization, also exhibit that descriptions generated by GPGT can be better accepted by real users.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"52-63"},"PeriodicalIF":4.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Platform-Driven Collaboration Patterns: Structural Evolution Over Time and Scale 平台驱动的协作模式：随时间和规模的结构演变

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-24 DOI: 10.1109/TCSS.2024.3452028

Negin Maddah;Babak Heydari

{"title":"Platform-Driven Collaboration Patterns: Structural Evolution Over Time and Scale","authors":"Negin Maddah;Babak Heydari","doi":"10.1109/TCSS.2024.3452028","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3452028","url":null,"abstract":"Within an increasingly digitalized organizational landscape, this research explores the dynamics of decentralized collaboration, contrasting it with traditional collaboration models. An effective capturing of high-level collaborations (beyond direct messages) is introduced as the network construction methodology including both temporal and content dimensions of user collaborations—an alternating timed interaction (ATI) metric as the first aspect, and a quantitative strategy of thematic similarity as the second aspect. This study validates three hypotheses that collectively underscore the complexities of digital team dynamics within sociotechnical systems. First, it establishes the significant influence of problem context on team structures in work environments. Second, the study reveals specific evolving patterns of team structures on digital platforms concerning team size and problem maturity. Last, it identifies substantial differences in team structure patterns between digital platforms and traditional organizational settings, underscoring the unexplored nature of digital collaboration dynamics. Focusing on Wikipedia's co-creation teams as a representative online platform, this study is instrumental for organizations navigating the digital era by identifying opportunities and challenges for managing information flow. The findings reveal significant collaborative potential and innovation in large online teams: the high speed of knowledge-sharing, numerous subcommunities, and highly decentralized leadership. This study paves the way for platform governors to design strategic interventions, tailored for different problem types, to optimize digital team dynamics and align them to broader organizational goals.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7814-7829"},"PeriodicalIF":4.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases 人类疾病早期检测的人工智能和区块链技术进展

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-24 DOI: 10.1109/TCSS.2024.3449748

Shumaiya Akter Shammi;Pronab Ghosh;Ananda Sutradhar;F M Javed Mehedi Shamrat;Mohammad Ali Moni;Thiago Eustaquio Alves de Oliveira

{"title":"Advances in Artificial Intelligence and Blockchain Technologies for Early Detection of Human Diseases","authors":"Shumaiya Akter Shammi;Pronab Ghosh;Ananda Sutradhar;F M Javed Mehedi Shamrat;Mohammad Ali Moni;Thiago Eustaquio Alves de Oliveira","doi":"10.1109/TCSS.2024.3449748","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3449748","url":null,"abstract":"Modern healthcare should include artificial intelligence (AI) technologies for disease identification and monitoring, particularly for chronic conditions, including heart, diabetes, kidney, liver, and thyroid. According to the World Health Organization (WHO), heart, diabetes, and liver diseases (hepatitis B and C and liver cirrhosis) are leading causes of mortality. The prevalence of thyroid and chronic kidney diseases is also increasing. We conducted a comprehensive review of the available literature to assess the current state of AI advancement in disease diagnosis and identify areas needing further attention. Machine learning (ML), deep learning (DL), and ensemble learning (EL) approaches have gained popularity in recent years due to their excellent results across various medical domains. This study focuses on their application in disease diagnosis and monitoring. We present a framework designed to provide aspiring researchers with a foundational understanding of popular algorithms and their significance in disease identification. Additionally, we highlight the importance of blockchain technology in the healthcare industry for safeguarding patient data confidentiality and privacy. The decentralized and immutable nature of blockchain can enhance data security, promote interoperability, and empower patients to control their medical information. By demonstrating the potential of advanced ML methods and blockchain technology to transform healthcare systems and improve patient outcomes, our research contributes to the field of disease diagnostics.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"210-237"},"PeriodicalIF":4.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling the Contributions of Participator, Content, and Network to Topic Duration in Online Social Group 在线社交群体中参与者、内容和网络对话题持续时间的贡献建模

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-24 DOI: 10.1109/TCSS.2024.3414586

Guoshuai Zhang;Jiaji Wu;Gwanggil Jeon;Penghui Wang;Yuan Chen;Yuhui Wang;Mingzhou Tan

{"title":"Modeling the Contributions of Participator, Content, and Network to Topic Duration in Online Social Group","authors":"Guoshuai Zhang;Jiaji Wu;Gwanggil Jeon;Penghui Wang;Yuan Chen;Yuhui Wang;Mingzhou Tan","doi":"10.1109/TCSS.2024.3414586","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3414586","url":null,"abstract":"As a common phenomenon that often appears on social platforms, news sites, and community forums, topics have played an irreplaceable role in public opinion and social governance. Meanwhile, people's daily lives are increasingly dependent on the breeding, transformation, and attenuation of hot topics. This article aims to discuss the problem about topic duration, that is, what are the principle factors that affect topic duration? Why do some topics survive longer and even generate subtopics, while other topics disappear rapidly? To answer these questions, we innovatively use 104 121 alliance chat content in \u0000<italic>Nova Empire II\u0000 from July 2023 to December 2023 as a case study. Dynamic topics trajectories are first obtained from a novel multilevel association model. Then, a potential factors system based on the dimensions of topic properties, topic users, and social network is established to quantitatively evaluate the influence for different factors. Experimental results from a robust statistical analysis framework demonstrate that higher topic discussion intensity, more content from opinion leader, faster information diffusion, and closer intertopic correlations will significantly improve the topic duration. Finally, a series of strategies are proposed to promote the design of social system applications from the perspectives of online social group.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7146-7158"},"PeriodicalIF":4.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CReMa: Crisis Response Through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media 通过计算识别和匹配跨语言请求和提供在社交媒体上共享的危机响应

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-24 DOI: 10.1109/TCSS.2024.3453226

Rabindra Lamsal;Maria Rodriguez Read;Shanika Karunasekera;Muhammad Imran

{"title":"CReMa: Crisis Response Through Computational Identification and Matching of Cross-Lingual Requests and Offers Shared on Social Media","authors":"Rabindra Lamsal;Maria Rodriguez Read;Shanika Karunasekera;Muhammad Imran","doi":"10.1109/TCSS.2024.3453226","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3453226","url":null,"abstract":"During times of crisis, social media platforms play a crucial role in facilitating communication and coordinating resources. In the midst of chaos and uncertainty, communities often rely on these platforms to share urgent pleas for help, extend support, and organize relief efforts. However, the overwhelming volume of conversations during such periods can escalate to unprecedented levels, necessitating the automated identification and matching of requests and offers to streamline relief operations. Additionally, there is a notable absence of studies conducted in multilingual settings despite the fact that any geographical area can have a diverse linguistic population. Therefore, we propose <underline>crisis <underline>response <underline>matcher (CReMa), a systematic approach that integrates textual, temporal, and spatial features to address the challenges of effectively identifying and matching requests and offers on social media platforms during emergencies. Our approach utilizes a crisis-specific pretrained model and a multilingual embedding space. We emulate human decision-making to compute temporal and spatial features and nonlinearly weigh the textual features. The results from our experiments are promising, outperforming strong baselines. Additionally, we introduce a novel multilingual dataset, simulating help-seeking and offering assistance on social media in 16 languages, and conduct comprehensive cross-lingual experiments. Furthermore, we analyze a million-scale geotagged global dataset to understand patterns in seeking help and offering assistance on social media. Overall, these contributions advance the field of crisis informatics and provide benchmarks for future research in the area.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"306-319"},"PeriodicalIF":4.5,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Extensible Bounded Rationality-Based Task Recommendation Scheme for From-Scratch Mobile Crowdsensing 一种基于可扩展有限理性的从头开始的移动众测任务推荐方案

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-23 DOI: 10.1109/TCSS.2024.3452099

Qiqi Shen;Miao Ma;Mengge Li

{"title":"An Extensible Bounded Rationality-Based Task Recommendation Scheme for From-Scratch Mobile Crowdsensing","authors":"Qiqi Shen;Miao Ma;Mengge Li","doi":"10.1109/TCSS.2024.3452099","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3452099","url":null,"abstract":"Mobile crowdsensing (MCS) has recently shown good performance in solving large-scale sensing tasks. As an essential topic in MCS, recommending tasks to participants has received extensive attention from researchers. Most studies assume that participants are absolutely rational, which is unrealistic because it is difficult for participants to know all the information about the transaction. Furthermore, most of them do not consider how to learn the preferences of new participants. In addition, their works are difficult to extend to different MCS scenarios. Considering the above problems, we propose an extensible bounded rationality-based task recommendation scheme (EBRTR), which contains a task recommendation framework and a bounded rationality decision-making model. First, a task recommendation framework that can be easily extended to various MCS scenarios is designed. Second, in our bounded rationality decision-making model, for participants with historical task information, according to the implicit information in their historical tasks, the human thinking mode with bounded rationality is simulated, and the improved classification and regression tree (ICART) algorithm is designed to construct the decision tree. For participants who newly join the platform, social information is introduced to construct an initial decision tree. Finally, extensive experimental evaluations demonstrate the effectiveness of the proposed scheme.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7871-7880"},"PeriodicalIF":4.5,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conceptualizing Resilience in Healthcare Systems Through Domain Modeling 通过领域建模概念化医疗保健系统中的弹性

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-17 DOI: 10.1109/TCSS.2024.3451519

Mie Wang;Junwei Wang;Hongfeng Wang

{"title":"Conceptualizing Resilience in Healthcare Systems Through Domain Modeling","authors":"Mie Wang;Junwei Wang;Hongfeng Wang","doi":"10.1109/TCSS.2024.3451519","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3451519","url":null,"abstract":"The healthcare system is a complex organization designed to provide medical services, exhibiting heterogeneity due to economic, legal, and cultural differences. To effectively manage the resilience of healthcare systems, it is essential to model their working domains. Furthermore, healthcare systems at different levels have distinct structures and substance flow characteristics. In particular, at the departmental level, the substance flow is primarily patient-centric, while at the hospital level, it includes orders, bills, and report flows. Interhospital scenarios may involve case flows and healthcare personnel movements. Therefore, this article proposes a healthcare system domain modeling framework based on the function-context-behavior-principle-state-structure (FCBPSS) tool, outlining its application in three hierarchical healthcare system levels. Unlike FCBPSS applications in other systems engineering fields, this framework not only captures the system's semantics but also encompasses ethical rules and operational standards within the healthcare system, making it an ideal tool for healthcare system domain modeling. Finally, the model's effectiveness is validated through the optimization of a case study at Sheng-Jing Hospital in Shenyang.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"113-127"},"PeriodicalIF":4.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0