Different strokes: differences in the characteristics and outcomes of BCVI and non-BCVI strokes in trauma patients by Lillian S Kao, Charles E Wade, Xu Zhang, Bryan A Cotton, Sean I Savitz, Michelle K McNutt, Cedar Slovacek, David Rosenbaum, Hari Kishan Reddy Indupuru, John Harvin

“…Blunt cerebrovascular injury (BCVI)–related strokes and mortality have decreased, likely due to refined screening and treatment algorithms in trauma literature; however, there is a paucity of research addressing non-BCVI strokes in trauma. …”
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Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden by László V. Módis, László V. Módis, András Matuz, András Matuz, Zsófia Aradi, Ildikó Fanny Horváth, Antónia Szántó, Antal Bugán

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Automated sample annotation for diabetes mellitus in healthcare integrated biobanking by Johannes Stolp, Christoph Weber, Danny Ammon, André Scherag, Claudia Fischer, Christof Kloos, Gunter Wolf, P. Christian Schulze, Utz Settmacher, Michael Bauer, Andreas Stallmach, Michael Kiehntopf, Boris Betz

“…Healthcare integrated biobanking describes the annotation and collection of residual samples from hospitalized patients for research purposes. The central idea of the current work is to establish an automated workflow for sample annotation, selection and storage for diabetes mellitus. …”
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The spatiotemporal distribution patterns and impact factors of bird species richness: A case study of urban built-up areas in Beijing, China by Zheran Zhai, Siyao Liu, Zimeng Li, Ruijie Ma, Xiaoyu Ge, Haidong Feng, Yang Shi, Chen Gu

“…Additionally, this study employed three tree-based machine learning algorithms—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—to investigate the influence of environmental factors on bird species distribution within urban built-up areas. …”
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Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA by Tong Y, Wen K, Li E, Ai F, Tang P, Wen H, Guo B

“…A LightGBM model was constructed and compared with other machine learning models, in terms of performance metrics such as the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). …”
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The outcome prediction method of football matches by the quantum neural network based on deep learning by Yang Sun, Hongyang Chu

“…This makes QNNs a superior choice compared to traditional neural networks and other advanced models for football match prediction. …”
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Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury by Chen Lin, Meng Zheng, Wensi Wu, Zhishan Wang, Guofeng Lu, Shaodan Feng, Xinlan Zhang

“…We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. …”
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Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition by Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee

“…Our comprehensive study provides a solid foundation for future research advancements in action recognition.…”
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Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

“…We prioritized key methylation sites through integrated analysis of methylation quantitative trait loci (meQTL), linkage disequilibrium (LD) patterns, and blood-brain methylation correlations. Machine learning algorithms generated diagnostic models, with classification performance evaluated using Area Under the Curve (AUC) metrics.Results: Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. …”
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Residual Film–Cotton Stubble–Nail Tooth Interaction Study Based on SPH-FEM Coupling in Residual Film Recycling by Xuejun Zhang, Yangyang Shi, Jinshan Yan, Shuo Yang, Zhaoquan Hou, Huazhi Li

“…Through analyses of the pickup device, key parameters were identified, and a model was built by combining the FEM and SPH algorithms to simulate the interaction of nail teeth, residual film, soil and root stubble. …”
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Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP by Bingkui Ren, Yuping Zhang, Siying Chen, Jinglong Dai, Junci Chong, Yifei Zhong, Mengkai Deng, Shaobo Jiang, Zhigang Chang

“…Model performance was compared​ to four other machine learning algorithms using the area under the curve (AUC). ​…”
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Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study by Huiyu Yao, Zixin Cao, Liangfu Huang, Haojie Pan, Xiaomin Xu, Fucai Sun, Xi Ding, Wan Wu

“…The Random Forest model demonstrated high accuracy (94.68%) in classifying disease groups, emphasizing the potential of machine learning to enhance diagnostic precision in oral mucosal diseases. Future research should focus on validating these findings in larger cohorts and exploring alternative machine-learning algorithms to improve diagnostic accuracy further.…”
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Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study by Chengkun Sun, Erin Mobley, Michael Quillen, Max Parker, Meghan Daly, Rui Wang, Isabela Visintin, Ziad Awad, Jennifer Fishe, Alexander Parker, Thomas George, Jiang Bian, Jie Xu

“…MethodsWe identified a cohort of patients under the age of 45 years from the OneFlorida+ Clinical Research Consortium. Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. …”
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Epidemiological association of the COVID-19 pandemic on Mycoplasma pneumoniae infections in children in Tianjin, China: a single-centre retrospective study (2017–2024) by Wei Wang, Lu Wang, Run Guo, Yun Bo, Yingxue Zou, Hualei Cui

“…Future research should focus on multicentre studies to validate these findings and explore the underlying mechanisms.…”
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Automated snow cover detection on mountain glaciers using spaceborne imagery and machine learning by R. Aberle, E. Enderlin, S. O'Neel, C. Florentine, L. Sass, A. Dickson, H.-P. Marshall, A. Flores

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External reference pricing for medicines in Ukraine: latest trends by L. I. Kucherenko, I. V. Nizhenkovska, N. V. Sholoiko, L. O. Hala, N. O. Datsiuk

“…Compared to the previous year, the share of healthcare expenditure in the state budget of Ukraine has decreased, which is associated with increased funding for the security and defense sector. …”
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Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea by Lifeng Li, Qimeng Shi, Bowen Fang, Yuting Liu, Xiang Liu, Yongqiang Shu, Yingke Deng, Yumeng Liu, Haijun Li, Junjie Zhou, Dechang Peng

“…The two‐sample t‐tests were conducted to compare FC values between subregions of M1 and other brain regions in two groups. …”
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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

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Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases ac... by Fuxu Wang, Yu Guo, Yuru Tang, Shuangmei Zhao, Kaige Xuan, Zhi Mao, Ruogu Lu, Rongyao Hou, Xiaoyan Zhu

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Comparison of cardiovascular risk prediction models developed using machine learning based on data from a Sri Lankan cohort with World Health Organization risk charts for predictin... by Anuradhani Kasturiratne, Hithanadura Janaka de Silva, Chamila Mettananda, Anuradha Supun Dassanayake, Norihiro Kato, Rajitha Wickremasinghe, Maheeka Solangaarachchige, Prasanna Haddela

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