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Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management

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Název: Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management
Autoři: Ali Fareedi, Abid, 1978, Ismail, Muhammad, Gagnon, Stephane, Ghazawneh, Ahmad, 1984, Arooj, Zartashia
Zdroj: Systems. 13(2):1-38
Témata: CRISP-KG, ontologies, knowledge graphs, SWRL, conversational agent, IDC
Popis: The research focuses on the limitations of traditional systems in optimizinginformation flow in the healthcare domain. It focuses on integrating knowledge graphs(KGs) and utilizing AI-powered applications, specifically conversational agents (CAs),particularly during peak operational hours in emergency departments (EDs). Leveragingthe Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designedto harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KGaugmentation empowers CAs with advanced reasoning, knowledge management, andcontext awareness abilities. We utilized a hybrid method integrating a participatory designcollaborative methodology (CM) and Methontology to construct a domain-centric robustformal ontological model depicting and mapping information flow during peak hours inEDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamlessinteraction with end users and enhancing the quality of care within EDs. The authorsleveraged semantic web rule language (SWRL) to enhance inferencing capabilities withinthe KG framework further, facilitating efficient information management for assistinghealthcare practitioners and patients. This innovative assistive solution helps efficientlymanage information flow and information provision during peak hours. It also leads tobetter care outcomes and streamlined workflows within EDs. © 2025 by the authors.
Popis souboru: electronic
Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-57376
https://doi.org/10.3390/systems13020072
Databáze: SwePub
Popis
Abstrakt:The research focuses on the limitations of traditional systems in optimizinginformation flow in the healthcare domain. It focuses on integrating knowledge graphs(KGs) and utilizing AI-powered applications, specifically conversational agents (CAs),particularly during peak operational hours in emergency departments (EDs). Leveragingthe Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designedto harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KGaugmentation empowers CAs with advanced reasoning, knowledge management, andcontext awareness abilities. We utilized a hybrid method integrating a participatory designcollaborative methodology (CM) and Methontology to construct a domain-centric robustformal ontological model depicting and mapping information flow during peak hours inEDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamlessinteraction with end users and enhancing the quality of care within EDs. The authorsleveraged semantic web rule language (SWRL) to enhance inferencing capabilities withinthe KG framework further, facilitating efficient information management for assistinghealthcare practitioners and patients. This innovative assistive solution helps efficientlymanage information flow and information provision during peak hours. It also leads tobetter care outcomes and streamlined workflows within EDs. © 2025 by the authors.
ISSN:20798954
DOI:10.3390/systems13020072