Development and Validation of DIANA (Diabetes Novel Subgroup Assessment tool): A web-based precision medicine tool to determine type 2 diabetes endotype membership and predict indi... by Viswanathan Baskar, Mani Arun Vignesh, Sumanth C Raman, Arun Jijo, Bhavadharini Balaji, Nico Steckhan, Lena Maria Klara Roth, Moneeza K Siddiqui, Saravanan Jebarani, Ranjit Unnikrishnan, Viswanathan Mohan, Ranjit Mohan Anjana

“…Its performance was compared with an algorithm determined based on conditional pre-determined cut-offs and weights for each clinical feature [age at diagnosis, BMI, waist, HbA1c, Serum Triglycerides, HDL-Cholesterol, (C-peptide fasting, C-peptide stimulated) - optional. …”
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Evaluation of Epidemiological Manifestations of Tuberculosis and Genetic Diversity of Mycobacterium tuberculosis Strains with Multiple Drug Resistance in the Omsk Region by I. V. Kostyukova, A. A. Vyazovaya, O. A. Pasechnik, I. V. Mokrousov

“…In accordance with the generally accepted algorithm of descriptive and evaluative epidemiological research, the incidence and prevalence of tuberculosis in the Omsk region for the period 2009–2023 were studied. …”
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Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning by Jan Li, Tim Coopmans, Patrick Emonts, Kenneth Goodenough, Jordi Tura, Evert van Nieuwenburg

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A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis by Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, Emmanuel Soriano Flores, Roberto Garcia Lara, Md. Abdus Samad, Imran Ashraf

“…The review focuses on examining stud-ies conducted between 2018 and 2023, sourced from three prominent academic databases. A meticulous three-stage approach was employed, encompassing the systematic planning, conduct, and analysis of the se-lected studies. …”
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Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability by Yasir Ibraheem Mohammed, Rosilah Hassan, Mohammad Kamrul Hasan, Shayla Islam, Huda Saleh Abbas, Muhammad Asghar Khan, Muhammad Attique Khan

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Predicting mechanical properties of low-alloy steels using features extracted from Electron Backscatter Diffraction characterization by Yu Li, Jingxiao Zhao, Xiucheng Li, Zhao Xing, Qiqiang Duan, Xiaojun Liang, Xuemin Wang

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A comprehensive review of data analytics and storage methods in geothermal energy operations by Ali Basem, Ahmed Kateb Jumaah Al-Nussairi, Dana Mohammad Khidhir, Narinderjit Singh Sawaran Singh, Mohammadreza Baghoolizadeh, Mohammad Ali Fazilati, Soheil Salahshour, S. Mohammad Sajadi, Ali Mohammadi Hasanabad

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Health economics evaluation of diagnostic strategies for gastro-oesophageal reflux disease with reflux symptoms in China: a modelling study by Yang Chen, Feng Xie, Hongda Chen, Dong Wu, Xiaoqing Li, Xiaxiao Yan, Meiduo Ouzhu, Ziqi Guo, Chengzhen Lyu, Daiyu Yang

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Population Cohort-Validated PM_2.5-Induced Gene Signatures: A Machine Learning Approach to Individual Exposure Prediction by Yu-Chung Wei, Wen-Chi Cheng, Pinpin Lin, Zhi-Yao Zhang, Chi-Hsien Chen, Chih-Da Wu, Yue Leon Guo, Hung-Jung Wang

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Predicting pathologic ≥N2 disease in women with breast cancer by Kerollos Nashat Wanis, Wenli Dong, Yu Shen, Funda Meric-Bernstam, Taiwo Adesoye, Henry M. Kuerer, Abigail S. Caudle, Nina Tamirisa, Sarah M. DeSnyder, Susie X. Sun, Isabelle Bedrosian, Puneet Singh, Solange E. Cox, Kelly K. Hunt, Rosa F. Hwang

“…Abstract The distinction between pN1 and ≥pN2 breast cancer impacts treatment decisions. Using data from a single institution on women with cN0 invasive breast cancer who were treated with upfront surgery, had 1-3 positive SLNs, and underwent completion ALND, we used gradient boosted trees (XGBoost) to develop a model for predicting ≥pN2 disease using clinicopathologic variables. …”
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Structural-methodical model of computer program for control of theoretical knowledge of cadets by V. V. Bulgakov

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Predicting Quality of Life in People Living with HIV: A Machine Learning Model Integrating Multidimensional Determinants by Meilian Xie, Zhiyun Zhang, Yanping Yu, Li Zhang, Jieli Zhang, Dongxia Wu

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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

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Opening closed doors: using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India by Anita Raj, Abhishek Singh, Nandita Bhan, Lotus McDougal, Nabamallika Dehingia, Julian McAuley

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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

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Classification of intracranial tumors based on optical-spectral analysis by I. D. Romanishkin, T. A. Savelieva, A. Ospanov, K. G. Linkov, S. V. Shugai, S. A. Goryajnov, G. V. Pavlova, I. N. Pronin, V. B. Loschenov

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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

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Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground‐Glass Nodules Based on AI‐Extracted Radiomic Features by Guozhen Yang, Yuanheng Huang, Huiguo Chen, Weibin Wu, Yonghui Wu, Kai Zhang, Xiaojun Li, Jiannan Xu, Jian Zhang

“…Nineteen radiomic features were extracted and filtered using Boruta and LASSO algorithms. Seven ML classifiers were evaluated using AUC‐ROC, decision curve analysis (DCA), and SHAP interpretability. …”
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Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

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Detecting and localizing cervical lesions in colposcopic images with deep semantic feature mining by Li Wang, Ruiyun Chen, Jingjing Weng, Huiping Li, Shi Ying, Jinghui Zhang, Zehao Yu, Chengbin Peng, Siming Zheng

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