Dirican, Nigar

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Profile URL
https://hdl.handle.net/20.500.14365/7245
Name Variants
Kazımzade, Nigar
Job Title
Email Address
nigar.dirican@ieu.edu.tr
Main Affiliation
09.02. Internal Sciences
Status
Former Staff
Website
ORCID ID
0000-0002-4815-6333
Scopus Author ID
55847250200
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
ETF-7314-2022

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  • Thumbnail Image
    Article
    Simultaneous Live Donor Liver Transplantation, Aortic Valve Replacement, and Atrial Septal Defect Repair in a Patient With End Stage Liver Disease: a Case Report
    (Elsevier Science Inc, 2023) Kazimi, Mirjalal; Beydullayev, Kamran; Farajov, Elnur; Shindiyeva, Saida; Jafarova, Shahnaz; Khalilov, Zaur; Nadirov, Tariyel; Dirican, Nigar
    Valvular heart disease creates an important barrier for orthotopic liver transplantation in patients with end-stage liver disease and increases mortality. Selection of the appropriate surgical scheme and adequate postoperative management can be lifesaving in these cases. This study presents a 32-year-old man diagnosed with hepatitis C-associated cirrhosis and severe aortic regurgitation due to subacute bacterial endocarditis. Initially, simultaneous aortic valve replacement (AVR) and live donor liver transplantation (LDLT) was planned. However, intraoperative transesopha- geal echocardiography revealed an additional atrial septal defect (ASD) and AVR, ASD repair, and LDLT surgery were performed. During the 2-year follow-up period, there were no early or late complications. To the best of our knowledge, this is the first patient to have simultaneous AVR, ASD repair, and LDLT surgery. Additionally, the present case is also unique in being the first person in the Republic of Azerbaijan to undergo concomitant cardiac surgery and LDLT.
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    Article
    A Bibliometric Analysis of Clinical Studies on Artificial Intelligence in Emergency Medicine
    (Lippincott Williams & Wilkins, 2025) Limon, Ö.; Bayram, B.; Çetin, M.; Limon, G.; Dirican, N.
    Background: Interest in artificial intelligence (AI) and machine learning (ML) has grown rapidly in recent years due to the success of modern algorithms across various domains. Emergency departments are fast-paced and resource-constrained environments where timely decision-making is critical. These characteristics make them ideal settings for the integration of AI technologies, which have shown potential to enhance diagnostic accuracy and optimize patient outcomes. This study aims to identify and characterize the scientific literature on AI and ML applications in emergency departments over the past decade. Methods: A comprehensive search was conducted in the Web of Science database on June 20, 2024. Articles published between 2015 and 2024 were considered. The search was performed using the keywords "artificial intelligence"or "machine learning"in all fields, limited to the "emergency medicine"category. The analysis of the articles included descriptive data on primary publication characteristics, such as the number of authors, citations, country of origin of the coauthors, and journal names. Bibliometric indicators were analyzed to identify publication trends and research themes, cluster analyses of keywords, and thematic maps. Results: A total of 321 articles were analyzed. The average number of citations per article was 10.04, and the annual growth rate was 37.87%. Most publications originated from the United States. Resuscitation, American Journal of Emergency Medicine, Injury-International Journal of the Care of the Injured, and Resuscitation Plus published 107 articles. In 2024, the trending topic of the articles was "health,"while "care"was the most popular in the last 10 years. The top 5 niche themes were "medical,""digital transformation,""education,""database,"and "emergency care systems."Conclusion: This bibliometric analysis highlights the growing role of AI in emergency medicine. The findings provide insight into current research directions and may help inform future investigations in this evolving field. © 2025 the Author(s).