Machine learning-based predictive model for acute pancreatitis-associated lung injury: a retrospective analysis by Zhaohui Du, Qiaoling Ying, Yisen Yang, Huicong Ma, Hongchang Zhao, Jie Yang, Zhenjie Wang, Chuanming Zheng, Shurui Wang, Qiang Tang

“…If successful, the tool may support early risk identification and guide interventions to prevent APALI.…”
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Developing Machine-Learning Models to Predict Bacteremia in Febrile Adults Presenting to the Emergency Department: A Retrospective Cohort Study from a Large Center by Chia-Ming Fu, Ike Ngo, Pak Sheung Lau, Yaroslav Ivanchuk, Fan-Ya Chou, Chih-Hung Wang, Chien-Yu Lin, Chu-Lin Tsai, Shey-Ying Chen, Tsung-Chien Lu, Hung-Yu Wei

“…Introduction: Bacteremia, a common disease but difficult to diagnose early, may result in significant morbidity and mortality without prompt treatment. …”
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Predictors of Successful Testicular Sperm Extraction: A New Era for Men with Non-Obstructive Azoospermia by Aris Kaltsas, Sofoklis Stavros, Zisis Kratiras, Athanasios Zikopoulos, Nikolaos Machairiotis, Anastasios Potiris, Fotios Dimitriadis, Nikolaos Sofikitis, Michael Chrisofos, Athanasios Zachariou

“…Integrating molecular biomarkers with artificial intelligence and machine learning algorithms may enhance predictive accuracy. <b>Conclusions</b>: Predicting TESE outcomes in men with NOA remains challenging using conventional clinical and hormonal parameters. …”
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Distinct brain atrophy progression subtypes underlie phenoconversion in isolated REM sleep behaviour disorderResearch in context by Stephen Joza, Aline Delva, Christina Tremblay, Andrew Vo, Marie Filiatrault, Max Tweedale, Jean-François Gagnon, Ronald B. Postuma, Alain Dagher, Johannes Klein, Michele Hu, Petr Dusek, Stanislav Marecek, Zsoka Varga, John-Paul Taylor, John T. O'Brien, Michael Firbank, Alan Thomas, Paul C. Donaghy, Stéphane Lehéricy, Marie Vidailhet, Jean-Christophe Corvol, ICEBERG Study Group, Richard Camicioli, Howard Chertkow, Simon Lewis, Elie Matar, Kaylena A. Ehgoetz Martens, Lachlan Churchill, Michael Sommerauer, Sinah Röttgen, Per Borghammer, Karoline Knudsen, Allan K. Hansen, Dario Arnaldi, Beatrice Orso, Pietro Mattioli, Luca Roccatagliata, Oury Monchi, Shady Rahayel, Isabelle Arnulf, Samir Bekadar, Eve Benchetrit, Alexis Brice, Vanessa Brochard, Alizé Chalançon, Benoit Colsch, Florence Cormier-Dequaire, Jean-Christophe Corvol, Virginie Czernecki, Bertrand Degos, Cécile Delorme, Pauline Dodet, Carole Dongmo-Kenfack, Marie-Odile Habert, Farid Ichou, Jonas Ihle, Cécile Galléa, Rahul Gaurav, Marie-Alexandrine Glachant, Manon Gomes, David Grabli, Elodie Hainque, Laetitia Jeancolas, Christelle Laganot, Stéphane Lehéricy, Suzanne Lesage, Smaranda Leu-Semenescu, Richard Levy, Valentine Maheo, Graziella Mangone, Louise Laure Mariani, Aurelie Méneret, Poornima Menon, Fanny Mochel, Vincent Perlbarg, Dijana Petrovska, Fanny Pineau, Nadya Pyatigorskaya, Sophie Rivaud-Pechoux, Emmanuel Roze, Sara Sambin, Julie Socha, Arthur Tenenhaus, Romain Valabregue, Marie Vidailhet, Lydia Yahia-Cherif

“…Distinct brain atrophy patterns may already be seen in iRBD; however, how brain atrophy begins and progresses remains unclear. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Additionally, 14 machine learning algorithms were trained and validated using SMOTE-balanced data and five-fold cross-validation. …”
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Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…These technologies not only enhance screening efficiency but also uncover molecular structures and properties that traditional methods may overlook. Specifically, ML models can extract key information and construct predictive models through feature extraction and pattern recognition using existing data. …”
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Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica M Veličković, Jacqueline Dinnes

“…Two SRs assessing machine learning-based algorithms reported high prognostic accuracy estimates, but some of which were sourced from the same data within which the models were developed, leading to potentially overoptimistic results. …”
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Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision me... by Yutong Fang, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, Jundong Wu

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…ConclusionDespite the potential of hybrid neural network models, as demonstrated in previous research, the findings of this study suggest that simpler neural network architectures, such as CNNs, may offer better prediction performance in certain scenarios. …”
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Defining analytical skills for human resources analytics: A call for standardization by Konrad Kulikowski

“…PURPOSE: Human resources (HR) analytics systems, powered by big data, AI algorithms, and information technology, are increasingly adopted by organizations to enhance HR’s impact on business performance. …”
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Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach by Li X, Xiao Y, Yang M, Zhang X, Yuan Z, Zhang Z, Zhang H, Liu L, Zhao M

“…Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. …”
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Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis by Jinyang Wang, Haonan Shi, Xiaowei Wang, Enhong Dong, Jian Yao, Yonghan Li, Ye Yang, Tingting Wang

“…Furthermore, advanced machine learning algorithms have provided fresh insights into the interactions among various risk factors. …”
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Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study by Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

“…Personalized, data-driven cervical cancer prevention (CCP) strategies tailored to phenotypic profiles may improve prevention and reduce disease burden. …”
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Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning by Shang S, Wei M, Lv H, Liang X, Lu Y, Tang B

“…Shuai Shang,1,2,&amp;ast; Meng Wei,1,2,&amp;ast; Huasheng Lv,1,2,&amp;ast; Xiaoyan Liang,1,2 Yanmei Lu,1,2 Baopeng Tang1,2 1Department of Cardiac Pacing and Electrophysiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China&amp;ast;These authors contributed equally to this workCorrespondence: Baopeng Tang, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email tangbaopeng1111@163.com Yanmei Lu, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email gracy@189.cnPurpose: This study aimed to develop and validate a model based on machine learning algorithms to predict the risk of in-hospital death among advanced elderly patients with Heart Failure (HF).Methods: A total of 4580 advanced elderly patients who were admitted to the hospital and diagnosed with HF from May 2012 to September 2023 were included in this study, among whom 552 cases (12.5%) died. …”
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Research and Design of an Active Light Source System for UAVs Based on Light Intensity Matching Model by Rui Ming, Tao Wu, Zhiyan Zhou, Haibo Luo, Shahbaz Gul Hassan

“…However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variations—the UAV’s visual features may degrade, especially under high-speed movement, ultimately resulting in failure of the vision tracking task and reducing the stability and robustness of swarm flight. …”
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The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT by Krzysztof Wach, Cong Doanh Duong, Joanna Ejdys, Rūta Kazlauskaitė, Pawel Korzynski, Grzegorz Mazurek, Joanna Paliszkiewicz, Ewa Ziemba

“…Contribution & Value Added: This article may aid in bringing attention to the significance of resolving the ethical and legal considerations that arise from the use of GAI and ChatGPT by drawing attention to the controversies and hazards associated with these technologies. …”
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Screening of potential markers for vitiligo based on bioinformatics and LASSO regression and prediction of Chinese medicine by Wei Liang, Minni Huang, Yue Sun, Shuyu Guan

“…Nineteen core genes were screened out from the five algorithms of Cytohubba, and LASSO regression analysis further determined four key genes (IL7R, GZMH, CD3G, and UBD). …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…With the help of machine learning algorithms, the VNF resource capacity demand prediction method VNFPre proposed for polymorphic network scenarios,it can judge the future VNF resource capacity demand of network slices, and provide a priori information for the placement and mapping of VNFs carried by network slices.…”
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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. …”
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Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis by Xiao-Zhi ZhaYang, Yan-Xiong Chen, Wen-Da Hua, Zheng-Lin Bai, Yun-Peng Jin, Xing-Wen Zhao, Quan-Fu Liu, Zeng-Dong Meng

“…Subsequently, by combining three machine learning algorithms to identify genes with highly correlated OA features. …”
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