Browsing by Author "Aygün İ."

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Determination of in-row seed distribution uniformity using image processing
    (Turkiye Klinikleri Journal of Medical Sciences, 2016) Çakir E.; Aygün İ.; Yazgi A.; Karabulut Y.
    The objective of this study was to determine the seed distribution uniformity of seeding machines using a low sensitivity (maximum 300 frames per second (fps)) high-speed camera and image processing method for corn, cotton, and wheat seeds under laboratory conditions. For this purpose, a high-speed camera with 100, 200, and 300 fps was used to measure the seed drop from the seeding tube onto the sticky belt. Video images then were transferred to the image processing algorithm, from which seed distribution can be calculated. The calculated measurements were compared statistically with the measurements obtained from sticky belt tests. According to the results for determining corn and cotton seed spacing by high-speed camera, the camera was successful only for corn seeds. For cotton seeds, camera readings were significantly different from the readings from the sticky belt due to the fact that capturing the cotton seed trajectory was not sufficient compared to the corn seed trajectory. Measuring the wheat seed spacing by high-speed camera was impossible with lower speeds of the camera. Wheat kernels could not be captured successfully by the camera at speeds of 100 and 200 fps. Therefore, only 300 fps speed was used to measure the seed spacing of wheat. © TUBİTAK.
  • No Thumbnail Available
    Item
    Identifying side effects of commonly used drugs in the treatment of Covid 19
    (Nature Research, 2020) Aygün İ.; Kaya M.; Alhajj R.
    To increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. As a result of the experiments, it has been found that the hematopoietic system and the cardiovascular system are exposed to more side effects than other organs. Among the focused drugs, Heparin and Atazanavir appear to cause more adverse reactions than other drugs. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient. © 2020, The Author(s).
  • No Thumbnail Available
    Item
    Use of Large Language Models for Medical Synthetic Data Generation in Mental Illness
    (Institution of Engineering and Technology, 2023) Aygün İ.; Kaya M.
    Data quantity and quality are very important for the development of medical artificial intelligence research. Nowadays, thanks to easier access to data, studies in this field produce very successful results. However, many factors such as protection of patient rights in medical data and confidentiality of personal data prevent researchers from directly accessing the data. For this reason, synthetic data creation studies are often needed both to expand the training and test sets and to create sample cases to be used in the relevant field. In this study, various synthetic patient data are created to be presented to a language model that enables the detection of psychological disorders through patient text. Synthetic data sets were produced with 200 artificial patient data created with popular LLM examples ChatGPT and Google Bard. The quality of synthetic data was measured with the help of a pre-trained BERT model using these datasets. In the experiments, it was observed that chatbots that generate instant data, such as ChatGPT and Google Bard, produced successful results at rates of 89% and 86% with the language representation model. With the experimental results, it appears that LLM studies can provide more successful results than advanced language models in various medical text production tasks. © The Institution of Engineering & Technology 2023.
  • No Thumbnail Available
    Item
    Identifying patients in need of psychological treatment with language representation models
    (Springer, 2025) Aygün İ.; Kaya B.; Kaya M.
    Early diagnosis of psychological disorders is very important for patients to regain their health. Research shows that many patients do not realize that they have a psychological disorder or apply to different departments for treatment. The detection of hidden psychological disorders in patients will both increase the quality of life of patients and reduce the traffic of patients who apply to the wrong department. This study aimed to determine whether patients who consult a physician for any reason need psychological treatment. For this purpose, the relationships, and similarities between the sentences of previous psychiatric patients and the sentences of newly arrived patients were analyzed. Domain-based trained ELECTRA language model was used to detect sentence similarities semantically. In the study, the dialogues of patients with physicians in 92 different specialties were analyzed using the MedDialog dataset, which consists of online physician applications, and the DAIC-WOZ dataset. As a result of the experiments, 90.49% success was achieved for the MedDialog dataset and 89.36% for the DAIC-WOZ dataset. With the proposed model, patients in need of psychological treatment were identified and the medical departments where psychological problems were revealed the most were determined. These divisions are Neurology, Sexology, Cardiology, and Plastic Surgery, respectively. With the findings obtained, complications caused by psychological problems and types of diseases that are precursors to psychological disorders were determined. To the best of our knowledge, this article is the first study that aims to analyze all psychological illness instead of focusing on any of the psychological problems (depression, OCD, schizophrenia, etc.) and validated by electronic health records. © The Author(s) 2024.