Browsing by Author "Atan, Suat"

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    Authors at the boundary: Interaction of local and general scientific literature
    (2018) Baskici, Cigdem; Atan, Suat; Ecil, Yavuz; 27467573
    How a scientific literature is shaped both at the local and general level is an important question to answer. One practical way of achieving this task is to identify the roles played by authors (academicians) as actors creating and disseminating knowledge in the relevant literature. To this end, this study examines roles undertaken by authors in the field of learning organizations. Identifying role typologies first requires revealing the author citation network in the field. Citation network is a matrix that summarizes citations and citation numbers between authors. To construct this matrix, articles in the local and general literature in the field of learning organizations, published and indexed between January 01, 2015 and February 20, 2017, were collected from Google Scholar by using the Java-based Selenium Library. An author citation network with 19,525 actors was created from this list. A social network analysis was conducted to identify author roles, followed by a discussion of what these roles mean for the local literature. Despite defining four typologies, observing only one type of role typology indicates that the local literature is not well integrated with the general literature. This study recommends strategic assessments for increasing the contribution ability of local literature to general one. Using this approach, it would be possible to find answers to the questions of which roles to develop via which authors and relationships, and how to remove the obstacles to development of local literature.
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    A novel prediction method for lymph node involvement in endometrial cancer: machine learning
    (2019) Gunakan, Emre; Atan, Suat; Haberal, Asuman Nihan; Kucukyildiz, Irem Alyazici; Gokce, Ehad; Ayhan, Ali; 30718313
    Objective The necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. In this study we constructed prediction models for EC patients using the Naive Bayes machine learning algorithm for LNI prediction. Methods The study assessed 762 patients with EC. Algorithm models were based on the following histopathological factors: V1: final histology; V2: presence of lymphovascular space invasion (LVSI); V3: grade; V4: tumor diameter; V5: depth of myometrial invasion (MI); V6: cervical glandular stromal invasion (CGSI); V7: tubal or ovarian involvement; and V8: pelvic LNI. Logistic regression analysis was also used to evaluate the independent factors affecting LNI. Results The mean age of patients was 59.1 years. LNI was detected in 102 (13.4%) patients. Para-aortic LNI (PaLNI) was detected in 54 (7.1%) patients, of which four patients had isolated PaLNI. The accuracy rate of the algorithm models was found to be between 84.2% and 88.9% and 85.0% and 97.6% for LNI and PaLNI, respectively. In multivariate analysis, the histologic type, LVSI, depth of MI, and CGSI were independently and significantly associated with LNI (p<0.001 for all). Conclusions Machine learning may have a place in the decision tree for the management of EC. This is a preliminary report about the use of a new statistical technique. Larger studies with the addition of sentinel lymph node status, laboratory findings, or imaging results with machine learning algorithms may herald a new era in the management of EC.