Health Information Science and Systems, Год журнала: 2023, Номер 11(1)
Опубликована: Ноя. 16, 2023
Язык: Английский
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Journal of Modelling in Management, Год журнала: 2025, Номер unknown
Опубликована: Фев. 14, 2025
Purpose The purpose of this study is to provide a holistic understanding the factors that either promote or hinder adoption artificial intelligence (AI) in supply chain management (SCM) and operations (OM). By segmenting AI lifecycle examining interactions between critical success failure factors, aims offer predictive insights can help proactively managing these ultimately reducing risk failure, facilitating smoother transition into AI-enabled SCM OM. Design/methodology/approach This develops knowledge graph model lifecycle, divided pre-development, deployment post-development stages. methodology combines comprehensive literature review for ontology extraction expert surveys establish relationships among ontologies. Using exploratory factor analysis, composite reliability average variance extracted ensures validity constructed dimensions. Pearson correlation analysis applied quantify strength significance entities, providing metrics labeling edges resource description framework. Findings identifies 11 dimensions integration OM: (1) setting clear goals standards; (2) ensuring accountable with leadership-driven strategies; (3) activating leadership bridge expertise gaps; (4) gaining competitive edge through partnerships advanced IT infrastructure; (5) improving data quality customer demand; (6) overcoming resistance via awareness benefits; (7) linking domain infrastructure robustness; (8) enhancing stakeholder engagement effective communication; (9) strengthening robustness change training governance; (10) using key performance indicators-driven reviews management; (11) accountability copyright integrity governance. Originality/value enhances decision-making by developing segments stages, introducing novel approach OM research. incorporating element uses graphs anticipate outcomes from These assist practitioners making informed decisions about use, overall
Язык: Английский
Процитировано
0
Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 16 - 25
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 118 - 127
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Artificial Intelligence Review, Год журнала: 2025, Номер 58(9)
Опубликована: Июнь 4, 2025
Язык: Английский
Процитировано
0
World Wide Web, Год журнала: 2022, Номер 25(3), С. 1243 - 1258
Опубликована: Март 16, 2022
Язык: Английский
Процитировано
12
Visual Computing for Industry Biomedicine and Art, Год журнала: 2023, Номер 6(1)
Опубликована: Ноя. 20, 2023
Knowledge graph (KG) fact prediction aims to complete a KG by determining the truthfulness of predicted triples. Reinforcement learning (RL)-based approaches have been widely used for prediction. However, existing largely suffer from unreliable calculations on rule confidences owing limited number obtained reasoning paths, thereby resulting in decisions Hence, we propose new RL-based approach named EvoPath this study. features reward mechanism based entity heterogeneity, facilitating an agent obtain effective paths during random walks. also incorporates postwalking leverage easily overlooked but valuable RL. Both mechanisms provide sufficient facilitate reliable confidences, enabling make precise judgments about Experiments demonstrate that can achieve more accurate predictions than approaches.
Язык: Английский
Процитировано
7
IEEE Journal of Biomedical and Health Informatics, Год журнала: 2023, Номер 27(10), С. 4649 - 4659
Опубликована: Янв. 2, 2023
New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems accessed through a booking centre managed by local authorities controlled the regional government. In perspective, structuring e-health data Knowledge Graph (KG) approach can provide feasible method to quickly simply organize and/or retrieve new information. Starting from raw bookings system in Italy, KG is presented support extraction of medical knowledge novel insights. By exploiting graph embedding which arranges various attributes entities into same vector space, we able apply Machine Learning (ML) techniques embedded vectors. The findings suggest that KGs could be used assess patients' patterns, either unsupervised or supervised ML. particular, former determine possible presence hidden groups not immediately available original legacy dataset structure. latter, although performance algorithms very high, shows encouraging results predicting patient's likelihood undergo particular visit year. However, many technological advances remain made, especially database algorithms.
Язык: Английский
Процитировано
6
Data Science and Engineering, Год журнала: 2023, Номер 8(2), С. 85 - 97
Опубликована: Апрель 26, 2023
Abstract Advanced knowledge engineering (KE), represented by graph (KG), drives the development of various fields and technologies provides fusion empowerment interfaces. At same time, advanced system (SE) takes model-based (MBSE) as core to realize formal modeling process analysis whole system. The two complement each other are key for transition from 2.0 3.0 in era artificial intelligence perceptual cognitive intelligence. This survey summarizes an information system, model-driven knowledge-enabled. Firstly, concept, representative methods, application introduced. Then, it introduces concept knowledge-driven engineering, architecture construction methods fields. Finally, combination systems, opportunities challenges discussed.
Язык: Английский
Процитировано
6
2022 IEEE Symposium on Computers and Communications (ISCC), Год журнала: 2023, Номер unknown, С. 1 - 7
Опубликована: Июль 9, 2023
The increasing use of electronic health records (EHRs) and wearable devices has led to the creation massive amounts personal data (PHD) that can be utilized for research patient care. However, managing integrating various types PHD from different sources poses significant challenges, including interoperability, privacy, security. To address these this paper proposes a blockchain-based knowledge graph integrated management. proposed approach utilizes graphs structure integrate PHD, such as EHR, sensing, insurance data, provide comprehensive view an individual's health. blockchain ensure privacy By storing on decentralized platform, patients have full control over their grant access specific entities needed providing enhanced
Язык: Английский
Процитировано
4