Diagnosis and treatment of the functional dyspepsia: clinical guidelines of the Russian Gastroenterological Association by V. T. Ivashkin, I. V. Mayev, A. A. Sheptulin, T. L. Lapina, A. S. Trukhmanov, I. M. Kartavenko, V. A. Kiprianis, O. Z. Okhlobystina

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Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms by Binglin Song, Ping Liu, Kangrui Fu, Chun Liu

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Machine Learning for the Prediction of Acute Kidney Injury in Critically Ill Patients With Coronary Heart Disease: Algorithm Development and Validation by Yike Li, Mingyang Xiao, Yaqian Li, Lulu Lv, Shanshan Zhang, Yuhui Liu, Juan Zhang

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Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men by Krittaka Soha, Sadiporn Phuthomdee, Thanapat Srichai, Lanchakorn Kittiratanawasin, Win Min Han, Sirinya Teeraananchai

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Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms by Hanqi Li, Jiayi Zhang, Yu Shi, Huanhuan Wang, Ruida Yang, Shaobo Wu, Yue Li, Xue Yang, Qingguang Liu, Liankang Sun

“…Abstract Background Matrix stiffness is strongly associated with hepatocarcinogenesis and significantly influences the properties of hepatocellular carcinoma (HCC). …”
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Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation by Julia Geppert, Peter Auguste, Asra Asgharzadeh, Hesam Ghiasvand, Mubarak Patel, Anna Brown, Surangi Jayakody, Emma Helm, Dan Todkill, Jason Madan, Chris Stinton, Daniel Gallacher, Sian Taylor-Phillips, Yen-Fu Chen

“…This is driven by costs and disutilities associated with false-positive results and CT surveillance. …”
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A model for assessing lethal resistance levels of various buildings based on improved genetic algorithm + BP neural network optimization by Jie Zhang, Jie Zhang, Bin Tan, Bin Tan, Chaoxu Xia, Wenbin Yan, Yuan Tao, Yuan Tao, Ben Ma, Ben Ma

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Machine learning for predicting medical outcomes associated with acute lithium poisoning by Omid Mehrpour, Varun Vohra, Samaneh Nakhaee, Seyed Ali Mohtarami, Farshad M. Shirazi

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Presenting the detection algorithm of all types of vessels in the Persian Gulf region using radar images in order to improve the security of navigation by Ali Hanafi, Mehdi Keykhaee

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Using machine learning algorithms (supervised) to generate automatically labeled dataset for detecting digital dating abuse from text messages by Tania Roy, Thomas Maranzatto, Zachary Loomas

“…This poster explores using machine learning algorithms trained on human-annotated datasets to label more extensive crowd-sourced datasets and generate a larger training dataset for abuse detection algorithms. …”
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Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers by Qianqian Guo, Peng Wu, Junhao He, Ge Zhang, Wu Zhou, Qianjun Chen

“…Logistic regression with forward selection and six other machine learning algorithms were employed to identify variables associated with breast cancer incidence. …”
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Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms by Hongjun Tao, Yang Wen, Rongfang Yu, Yining Xu, Fangliang Yu

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Development of a prognostic model for breast cancer patients based on intratumoral tumor-infiltrating lymphocytes using machine learning algorithms by Xinyi Wu, Chun Li

“…Employing consensus clustering and Weighted Correlation Network Analysis (WGCNA), we identified iTIL-associated hub genes. Our iTIL-centric signature was developed using a machine learning framework integrating 101 algorithms, validated across independent testing sets. …”
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Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study by Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang

“…Abstract Kinesiophobia is particularly common in postoperative lung cancer patients, which causes patients may be reluctant to cough and move due to misperception, internal fear or fear of pain, and avoid rehabilitation training affecting postoperative recovery. Therefore, it is clinically important to discover the factors associated with the occurrence of kinesiophobia and to develop a prediction model. …”
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms by Chenhui Feng, Zhimiao Wei, Xiaohui Li, Xiaohui Li

“…Through differential expressed genes (DEG) analysis, high-dimensional Weighted Correlation Network Analysis (hdWGCNA) and machine learning algorithms, we identified signature genes associated with both BAM and FAM. …”
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Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration by Qiang Liu, Hua Zhang, Heng Xiao, Ao Ren, Ying Cai, Rui Liao, Huarong Yu, Zhongjun Wu, Zuotian Huang

“…Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers.Materials and methods In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. …”
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Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition by Ahmed Elbeltagi, Okan Mert Katipoğlu, Veysi Kartal, Ali Danandeh Mehr, Sabri Berhail, Elsayed Ahmed Elsadek

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Re-identification assistance and multi-stage association for pedestrian multi-object tracking by Ye Li, Li Zhan, Lei Wu, Hongkun Liu, Jianli Min, Xinzhong Wang

“…Therefore, we design a multi-object pedestrian tracking method that combines re-identification feature assistance and multi-stage data association (RAMA). This method innovatively focuses on the role of low confidence bounding boxes in MOT, and introduces a separately trained pedestrian re-identification model to extract discriminative features of pedestrians, then adds this feature to the multi-stage data association algorithm to improve the accuracy of multi-object tracking. …”
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Machine learning analysis of cardiovascular risk factors and their associations with hearing loss by Ali Nabavi, Farimah Safari, Ali Faramarzi, Mohammad Kashkooli, Meskerem Aleka Kebede, Tesfamariam Aklilu, Leo Anthony Celi

“…The National Health and Nutrition Examination Survey (NHANES) 2012–2018 data comprising audiometric tests and cardiovascular risk factors was utilized. Machine learning algorithms were trained to classify hearing impairment thresholds and predict pure tone average values. …”
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Modeling of train flow handling through a limiting single-track section of the route at the organization of high-speed operation using the existing infrastructure by S. V. Karasev, A. D. Kalidova

“…The model implements an algorithm that does not impose any restrictions on the characteristics of the incoming train flow, which makes it possible to investigate the various conditions for trains to pass through the limiting element (in particular, the regularity or irregularity of the flow, the intervals between trains, the occupation times of the limiting element). …”
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