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• Alfaro-Saíz, Juan-Jose(1)
• Arias-Fonseca, Sebastián(1)
• Barbati, Maria(1)
• Gul, Muhammet(1)
• Ishizaka, Alessio(1)
• Khan, Jehangir(1)
• Ortiz-Barrios, Miguel(1)
• Pérez-Aguilar, Armando(1)
• Uyan, Umut(1)
• Yücesan, Melih(1)

• MakaleAçık Erişim2024Scopus SJRQ1/r/munzur/1705
  Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons

  Ortiz-Barrios, Miguel; Ishizaka, Alessio; Barbati, Maria; Arias-Fonseca, Sebastián; Khan, Jehangir; Gul, Muhammet; Yücesan, Melih; Alfaro-Saíz, Juan-Jose; Pérez-Aguilar, Armando

  Seasonal Respiratory Diseases (SRDs) usually produce a heightened number of Emergency Department (ED) attendances due to their rapid dissemination within the community and the ineffective prevention measures. Such a context requires effective management of the emergency care processes to provide in-time diagnosis and treatment to infected patients. Nonetheless, EDs have evidenced severe operational deficiencies during these periods, thereby provoking extended bed waiting times in Hospitalization Departments (HDs). Therefore, this paper presents a hybrid approach merging Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to shorten the bed waiting times in HDs considering patient records collated in the first emergency care stages. First, we implemented Random Forest (RF) to estimate the probability of respiratory worsening based on sociodemographic and clinical patient data. Second, we inserted these probabilities into a DES model mimicking the emergency care from the admission to the HD. We then pretested different HD configurations and strategies seeking to reduce the HD bed waiting time. A case study of a European hospital group was used to validate the suggested framework. The AI-DES model enabled decision-makers to identify an improvement proposal with hospitalization bed waiting time lessening, oscillating between 7.93 and 7.98 h.

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• MakaleAçık Erişim2023/r/munzur/4821
  Yapay Zekânın Bilimsel Yayın Amaçlı Kullanımına İlişkin Etik Kaygılar: Sistematik Bir Yazın İncelemesi

  Uyan, Umut

  Although artificial intelligence (AI) is a relatively new technology, it claims to change and transform our business practices. This novel technology, which offers tools that are easy-to-use, fast, and tailor-made outputs in terms of efficiency and productivity, is also becoming more prevalent in higher education. Nonetheless, some ethical issues need to be addressed regarding the use of these technologies for both education and research purposes. This study, designed as a systematic literature review, aims to reveal the ethical concerns regarding such technologies used in scientific publishing activities. Five distinct themes have emerged echoing ethical concerns regarding AI use in scientific publications: Artificially guided research, the illusion of velocity , bias , data quality , and ready-made science. The study serves as a wake-up call to researchers to approach such technologies not only from a cost-benefit perspec-tive, but also to examine potential ethical considerations. Among the pioneering studies on AI usage for scientific publication in higher education, this study will add to the body of knowledge.

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