Patryk Zywica

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OBJECTIVES The aim of this study was to externally validate the diagnostic performance of the International Ovarian Tumor Analysis logistic regression models (LR1 and LR2, 2005) and other popular prognostic models including the Timmerman logistic regression model (1999), the Alcazar model (2003), the risk of malignancy index (RMI, 1990), and the risk of(More)
This article describes the fundamentals of an intelligent decision support system for the diagnosis of ovarian tumors. The system is designed to support diagnosis by less experienced gynecologists, and to gather data for continuous improvement of the quality of diagnosis. The theoretical basis for the construction of the system is the IF-sets framework,(More)
OBJECTIVES The external, two-center validation of the IOTA ADNEX model for differential diagnosis of adnexal tumors. METHODS A total of 204 patients with adnexal masses (134 benign and 70 malignant) treated at the Division of Gynecologic Surgery, Poznan University of Medical Sciences, Poland (Center I), and 123 patients (89 benign and 34 malignant) from(More)
In the paper we present OvaExpert a unique tool for supporting gynecologists in the diagnosis of ovarian tumor, combining classical diagnostic scales with modern methods of machine learning and soft computing. A distinguishing feature of the system is its comprehensiveness, which makes it usable at any stage of a diagnostic process. We gather all the(More)
This paper presents an approach to making accurate and high-quality decisions under incomplete information. Our comprehensive approach includes interval modeling of incomplete data, uncertaintification of classical models and aggregation of incomplete results. We conducted a thorough evaluation of our approach using medical data for ovarian tumor diagnosis,(More)
In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data which is an unique feature of(More)
Dear Editor, We carefully read the letter by Van Calster et al. (Van Calster et al., 2016a) concerning our article (Van Calster et al., 2016a; Szubert et al., 2016). We want to thank the authors for the valuable feedback on our work, and we do appreciate it. However, some of the guidelines mentioned by the authorswere recently published, hence, we could not(More)
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