F
Farahnaz Sadoughi
Researcher at Iran University of Medical Sciences
Publications - 120
Citations - 1739
Farahnaz Sadoughi is an academic researcher from Iran University of Medical Sciences. The author has contributed to research in topics: Health care & Information system. The author has an hindex of 19, co-authored 110 publications receiving 1258 citations. Previous affiliations of Farahnaz Sadoughi include Tehran University of Medical Sciences & Tabriz University of Medical Sciences.
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Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients
TL;DR: The study showed that comorbidity states, such as lung disorders and hemorrhage with drug consumption have an impact on long LOS, and there was a significant tendency for LOS to be longer in patients with lung or respiratory disorders and high blood pressure.
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Internet of things in medicine: A systematic mapping study
TL;DR: This systematic mapping study identified and map the current IoT developments in medicine through providing graphical/tabular classifications on the current experimental and practical IoT information in medicine, the involved medical sub-fields, the locations of IoT use in Medicine, and the bibliometric information about IoT research articles.
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Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets
TL;DR: This paper introduces a novel partitioning algorithm that its initialization does not lead the algorithm to local optimum and can find all the Gaussian-shaped clusters if it has the right number of them.
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Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review.
Farahnaz Sadoughi,Zahra Kazemy,Farahnaz Hamedan,Leila Owji,Meysam Rahmanikatigari,Tahere Talebi Azadboni +5 more
TL;DR: This article examined various methods of AI using image processing to diagnose breast cancer through library and Internet searches and showed that support vector machines had the highest accuracy percentage for different types of images.
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Developing and Using Expert Systems and Neural Networks in Medicine: A Review on Benefits and Challenges
TL;DR: It is concluded that expert systems and neural networks can be successfully used in medicine; however, there are many concerns and questions to be answered through future studies and discussions.