PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections
Permanent URI for this collectionhttps://hdl.handle.net/20.500.13091/5
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Browsing PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections by WoS Q "N/A"
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Article Citation - WoS: 11Citation - Scopus: 19Artificial Intelligence in Healthcare Competition (teknofest-2021): Stroke Data Set(AVES, 2022) Koç, U.; Sezer, E.A.; Özkaya, Y.A.; Yarbay, Y.; Taydaş, O.; Ayyıldız, V.A.; Bahadır, MuratObjective: The artificial intelligence competition in healthcare was organized for the first time at the annual aviation, space, and technology festival (TEKNOFEST), Istanbul/Türkiye, in September 2021. In this article, the data set preparation and competition processes were explained in detail; the anonymized and annotated data set is also provided via official website for further research. Materials and Methods: Data set recorded over the period covering 2019 and 2020 were centrally screened from the e-Pulse and Teleradiology System of the Republic of Türkiye, Ministry of Health using various codes and filtering criteria. The data set was anonymized. The data set was prepared, pooled, curated, and annotated by 7 radiologists. The training data set was shared with the teams via a dedicated file transfer protocol server, which could be accessed using private usernames and passwords given to the teams under a non-disclosure agreement signed by the representative of each team. Results: The competition consisted of 2 stages. In the first stage, teams were given 192 digital imaging and communications in medicine images that belong to 1 of 3 possible categories namely, hemorrhage, ischemic, or non-stroke. Teams were asked to classify each image as either stroke present or absent. In the second stage of the competition, qualifying 36 teams were given 97 digital imaging and communications in medicine images that contained hemorrhage, ischemia, or both lesions. Among the employed methods, Unet and DeepLabv3 were the most frequently observed ones. Conclusion: Artificial intelligence competitions in healthcare offer good opportunities to collect data reflect-ing various cases and problems. Especially, annotated data set by domain experts is more valuable. © 2022, AVES. All rights reserved.Conference Object Citation - Scopus: 4Deep Learning-Based Brain Hemorrhage Detection in Ct Reports(IOS Press BV, 2022) Bayrak, Gıyaseddin; Toprak, M. Şakir; Ganiz, Murat Can; Kodaz, Halife; Koç, UralRadiology reports can potentially be used to detect critical cases that need immediate attention from physicians. We focus on detecting Brain Hemorrhage from Computed Tomography (CT) reports. We train a deep learning classifier and observe the effect of using different pre-trained word representations along with domain-specific fine-tuning. We have several contributions. Firstly, we report the results of a large-scale classification model for brain hemorrhage detection from Turkish radiology reports. Second, we show the effect of fine-tuning pre-trained language models using domain-specific data on the performance. We conclude that deep learning models can be used for detecting brain Hemorrhage with reasonable accuracy and fine-tuning language models using domain-specific data to improve classification performance. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.Article Citation - Scopus: 2Direct or Dna Extraction-Free Amplification and Quantification of Foodborne Pathogens(2025) Williams M.R.; Telli A.E.; Telli N.; Islam D.T.; Hashsham S.A.The use of direct nucleic acid amplification of pathogens from food matrices has the potential to reduce time to results over DNA extraction-based approaches as well as traditional culture-based approaches. Here we describe protocols for assay design and experiments for direct amplification of foodborne pathogens in food sample matrices using loop-mediated isothermal amplification (LAMP) and polymerase chain reaction (PCR). The examples provided include the detection of Escherichia coli in milk samples and Salmonella in pork meat samples. This protocol includes relevant reagents and methods including obtaining target sequences, assay design, sample processing, and amplification. These methods, though used for specific example matrices, could be applied to many other foodborne pathogens and sample types. © 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.Article Electrochemical Detection of Nucleic Acids Using Three-Dimensional Graphene Screen-Printed Electrodes(2025) Islam D.T.; Mobasser S.; Kotaru S.; Telli A.E.; Telli N.; Cupples A.M.; Hashsham S.A.Electrochemical approaches, along with miniaturization of electrodes, are increasingly being employed to detect and quantify nucleic acid biomarkers. Miniaturization of the electrodes is achieved through the use of screen-printed electrodes (SPEs), which consist of one to a few dozen sets of electrodes, or by utilizing printed circuit boards. Electrode materials used in SPEs include glassy carbon (Chiang H-C, Wang Y, Zhang Q, Levon K, Biosensors (Basel) 9:2-11, 2019), platinum, carbon, and graphene (Cheng FF, He TT, Miao HT, Shi JJ, Jiang LP, Zhu JJ, ACS Appl Mater Interfaces 7:2979-2985, 2015). There are numerous modifications to the electrode surfaces as well (Cheng FF, He TT, Miao HT, Shi JJ, Jiang LP, Zhu JJ, ACS Appl Mater Interfaces 7:2979-2985, 2015). These approaches offer distinct advantages, primarily due to their demonstrated superior limit of detection without amplification. Using the SPEs and potentiostats, we can detect cells, proteins, DNA, and RNA concentrations in the nanomolar (nM) to attomolar (aM) range. The focus of this chapter is to describe the basic approach adopted for the use of SPEs for nucleic acid measurement. © 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.Article A Multidimensional Analysis of the 21st Century Competencies Scale through AI-Driven Data Mining Techniques(Nature Research, 2025) Koklu, N.In recent years, evaluating competencies such as knowledge, practical skills, character traits, and meta-learning capabilities has gained increasing importance in educational research. As educational datasets grow larger and more complex, machine learning offers promising tools for analyzing student responses and identifying patterns that support assessment processes. This study aims to classify student responses collected through the 21st Century Competencies Scale using a variety of machine learning algorithms, including SVM, ANN, k-NN, RF, LR, DT, AdaBoost, Gradient Boosting, and XGBoost. The dataset contains responses from 616 participants and covers four key sub-dimensions. Model performance was measured using accuracy, precision, recall, and F1-score. Grid search optimization was also applied to improve performance. The highest classification accuracy was achieved by LR in the “Character” sub-dimension (78.73%), followed by SVM in the “Skills” (78.58%) and overall scale (74.51%). Gradient Boosting and k-NN models also showed competitive results across multiple dimensions. These findings emphasize the effectiveness of machine learning, particularly when combined with parameter optimization, in supporting data-driven educational assessments. © The Author(s) 2025.Article Search for New Physics in Jet Multiplicity Patterns of Multilepton Events at (Formula Presented)(American Physical Society, 2025) Hayrapetyan, A.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Bergauer, T.; Chatterjee, S.; Makarenko, V.A first search for beyond the standard model physics in jet multiplicity patterns of multilepton events is presented, using a data sample corresponding to an integrated luminosity of (Formula presented) of 13 TeV proton-proton collisions recorded by the CMS detector at the LHC. The search uses observed jet multiplicity distributions in one-, two-, and four-lepton events to explore possible enhancements in jet production rate in three-lepton events with and without bottom quarks. The data are found to be consistent with the standard model expectation. The results are interpreted in terms of supersymmetric production of electroweak chargino-neutralino superpartners with cascade decays terminating in prompt hadronic (Formula presented)-parity violating interactions. © 2025 CERN, for the CMS Collaboration.Article Citation - Scopus: 1Search for the Rare Decay (Formula Presented) in Proton-Proton Collisions at (Formula Presented)(American Physical Society, 2025) Tchekhovski, V.; Hayrapetyan, A.; Makarenko, V.; Tumasyan, A.; Adam, W.; Andrejkovic, J.W.; Druzhkin, D.A search for the rare decay (Formula presented) is reported using proton-proton collision events at (Formula presented) collected by the CMS detector in 2022-2023, corresponding to an integrated luminosity of (Formula presented). This is the first analysis to use a newly developed inclusive dimuon trigger, expanding the scope of the CMS flavor physics program. The search uses (Formula presented) mesons obtained from (Formula presented) decays. No significant excess is observed. A limit on the branching fraction of (Formula presented) at 95% confidence level is set. This is the most stringent upper limit set on any flavor changing neutral current decay in the charm sector. © 2025 CERN, for the CMS Collaboration.
