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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Identification and validation of an explainable machine learning model for vascular depression diagnosis in the older adults: a multicenter cohort study by Ran Zhang, Tian Li, Fan Fan, Haoying He, Liuyi Lan, Dong Sun, Zhipeng Xu, Sisi Peng, Jing Cao, Juan Xu, Xiaoxiang Peng, Ming Lei, Hao Song, Junjian Zhang

“…The final model also achieved, and marginally exceeded, clinician-level diagnostic performance. Conclusions Our research established a consistent and explainable ML framework for identifying VaDep in older adults, utilizing comprehensive clinical data. …”
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Co-Occurring Diseases and Mortality in Patients With Chronic Heart Disease, Modeling Their Dynamically Expanding Disease Portfolios: Nationwide Register Study by Nikolaj Normann Holm, Anne Frølich, Helena Dominguez, Kim Peder Dalhoff, Helle Gybel Juul-Larsen, Ove Andersen, Anders Stockmarr

“…MethodsThis study used data from the national Danish registries and algorithmic diagnoses of 15 chronic diseases to obtain a study population of all 766,596 adult patients with HD in Denmark from January 1, 1995, to December 31, 2015. …”
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Safety assessment of temozolomidee: real-world adverse event analysis from the FAERS database by Yu Liu, Lan Ma, Lan Ma, Xiaojia Fu, Xiaojia Fu, Yi Zhang, Yi Zhang, Jinyu Zheng, Zhongjun Chen

“…Future studies should validate these signals through prospective trials and mechanistic research to optimize TMZ’s risk-benefit profile in glioma therapy.…”
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Global overview of usable Landsat and Sentinel-2 data for 1982–2023Dryad by Katarzyna Ewa Lewińska, Stefan Ernst, David Frantz, Ulf Leser, Patrick Hostert

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Control Tolerante a Fallas Activo: Estimación y acomodación de fallas en sensores aplicado al modelo LPV de una bicicleta sin conductor by J.A. Brizuela-Mendoza, C.M. Astorga-Zaragoza, A. Zavala-Río, F. Canales-Abarca

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Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study by Xinxin Qin, Binxu Qiu, Litao Ge, Song Wu, Yuye Ma, Wei Li

“…We applied six machine learning algorithms to construct a model that can predict distant metastasis of gastric cancer. …”
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ANALISIS PROSES BERPIKIR KOMPUTASI SISWA PADA MATERI BARISAN ARITMETIKA BERDASARKAN KECERDASAN MAJEMUK by Rosevita Melati, Toto Nusantara, Mochammad Hafiizh

“…This study aims to describe the process of computational thinking in arithmetic sequences based on multiple intelligences. This research is a qualitative descriptive study. Participants in this study were selected using purposive sampling techniques based on students' multiple intelligences. …”
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Impacts of air fraction increase on Arctic sea ice density, freeboard, and thickness estimation during the melt season by E. Salganik, O. Crabeck, O. Crabeck, N. Fuchs, N. Hutter, N. Hutter, P. Anhaus, P. Anhaus, J. C. Landy

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SK-TreePCN: Skeleton-Embedded Transformer Model for Point Cloud Completion of Individual Trees from Simulated to Real Data by Haifeng Xu, Yongjian Huai, Xun Zhao, Qingkuo Meng, Xiaoying Nie, Bowen Li, Hao Lu

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Transient Attracting Profiles in the Great Pacific Garbage Patch by L. Kunz, A. Griesel, C. Eden, R. Duran, R. Duran, B. Sainte-Rose

“…But we also find TRAPs that retain material over multiple weeks where we suspect material clustering at the submesoscale. Prospective research could investigate this further by applying our algorithms to soon-available high-resolution observations of the flow. …”
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Transformer Models improve the acoustic recognition of buzz-pollinating bee species by Alef Iury Siqueira Ferreira, Nádia Felix Felipe da Silva, Fernanda Neiva Mesquita, Thierson Couto Rosa, Stephen L. Buchmann, José Neiva Mesquita-Neto

“…We document the potential of transformer architectures to improve the performance of audible bee species identification, offering promising new avenues for bioacoustic research and pollination ecology.…”
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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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Machine Learning and Interpretability Study for Predicting 30-Day Unplanned Readmission Risk of Schizophrenia: A Retrospective Study by Tan Y, Chen G, Wang S, Zhan X, Cheng R, Qiao L, Zhang Z, Liu Y

“…The model was constructed using five ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). …”
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Integrating multi-omics and machine learning strategies to explore the “gene-protein-metabolite” network in ischemic heart failure with Qi deficiency and blood stasis syndrome by Jingjing Wei, Aolong Wang, Peng Yu, Yang Sun, Wenjun Wu, Yilin Zhang, Rui Yu, Bin Li, Mingjun Zhu

“…These findings may provide novel insights and methods for further advancing this research field. Graphical Abstract…”
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The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review by Norah Hamad Alhumaidi, Doni Dermawan, Hanin Farhana Kamaruzaman, Nasser Alotaiq

“…The search focused on extracting data regarding the ML algorithms applied; disease categories studied; types of study designs (eg, clinical trials and cohort studies); and the sources of RWE, including EHRs, patient registries, and wearable devices. …”
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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

“…This study not only deepens understanding of the factors affecting sleep quality in OSA patients, but also provides a powerful predictive tool for clinical doctors. Future research can explore the potential of incorporating these predictive factors into comprehensive treatment strategies to improve patient prognosis and overall quality of life.Keywords: machine learning, risk prediction, sleep quality, OSA, SHAP…”
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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

“…Furthermore, future research can further explore the integration of QNN models with mathematical analysis systems, expanding their application scenarios in the real world. …”
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Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. …”
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Methodology of Determining the Location of the Distribution Center of Material Flows in a Combined Scheme of Goods Delivery by Svetlana A. Zhestkova

“…Introduction. The research is aimed at developing a methodology for determining the optimal location of material flows’ distribution center, taking into account the formation of ring routes based on the use of the fictitious nodes and branches’ method (FNB), in order to minimize overall logistical costs. …”
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