Machine Learning-Based Prediction Model for Multidrug-Resistant Organisms Infections: Performance Evaluation and Interpretability Analysis by Zhao W, Sun P, Li W, Shang L

“…Following TRIPOD guidelines, key predictors were identified using Lasso regression from a comprehensive set of clinical variables, including demographics, treatments, and laboratory data. Six machine learning algorithms—Neural Networks, Random Forests, Support Vector Machines, Logistic Regression, Decision Trees, and Gaussian Naive Bayes—were evaluated based on AUC, accuracy, and calibration curves. …”
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Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis by Feng Pang, Lijiao Wu, Jianping Qiu, Yu Guo, Liangen Xie, Shimin Zhuang, Mengya Du, Danni Liu, Chenyue Tan, Tianrun Liu

“…The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed.…”
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Exploration the role of pro-inflammatory fibroblasts and related markers in periodontitis: combing with scRNA-seq and bulk-seq data by Erli Wu, Feihan Gu, Qiangqiang Zhuo, Ziyang Gao, Yu Zhang, Jingjing Li, Xuan Yin, Weimin Bao, Xianqing Zhou, Feng Liang, Shouxiang Yang, Yuanyin Wang, Qingqing Wang, Qingqing Wang, Wei Shao, Wei Shao

“…Differential gene analysis, WGCNA, and machine learning algorithms identified two genes (MME and TSPAN11) as potential therapeutic targets for periodontitis. …”
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Research on multi-algorithm and explainable AI techniques for predictive modeling of acute spinal cord injury using multimodal data by Jiaojiao Tai, Linbang Wang, Yijun Xie, Yang Li, Hua Fu, Xiaowen Ma, Haiyan Li, Xinying Li, Ziqiang Yan, Jingkun Liu

“…A total of 387 ASCI patients were included, with clinical, imaging, and laboratory data collected. Key features were selected using univariate analysis, Lasso regression, and other feature selection techniques, integrating clinical, radiomics, and laboratory data. …”
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Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis by Razvan Onciul, Razvan Onciul, Felix-Mircea Brehar, Felix-Mircea Brehar, Adrian Vasile Dumitru, Adrian Vasile Dumitru, Carla Crivoi, Razvan-Adrian Covache-Busuioc, Razvan-Adrian Covache-Busuioc, Matei Serban, Matei Serban, Petrinel Mugurel Radoi, Petrinel Mugurel Radoi, Corneliu Toader, Corneliu Toader

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Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis by Jin-Xin Zheng, Hui‐Hui Zhu, Shang Xia, Men‐Bao Qian, Robert Bergquist, Hung Manh Nguyen, Xiao‐Nong Zhou

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Self-supervised learning analysis of multi-FISH labeled cell-type map in thick brain slices by Weijie Zheng, Weijie Zheng, Yiping An, Yiping An, Kang Li, Jinyue Wang, Jianqing Gao, Huawei Mu, Huawei Mu, Jin Tang, Jin Tang, Hao Wang, Hao Wang

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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…This study is the first to systematically evaluate the performance of ARDS prediction models based on multiple quantitative data sources. We compare the effectiveness of ML models via a meta-analysis, revealing factors affecting performance and suggesting strategies to enhance generalization and prediction accuracy. …”
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Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait by Wenhao Qi, Shiying Shen, Chaoqun dong, Mengjiao Zhao, Shuaiqi Zang, Xiaohong Zhu, Jiaqi Li, Bin Wang, Yankai Shi, Yongze Dong, Huajuan Shen, Junling Kang, Xiaodong Lu, Guowei Jiang, Jingsong Du, Eryi Shu, Qingbo Zhou, Jinghua Wang, Shihua Cao

“…Future research will likely progress toward deeper applications of artificial intelligence, enhanced interinstitutional collaboration, comprehensive analysis of different data types, and the exploration of digital biomarkers for a broader range of Parkinson symptoms. …”
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Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning by Wenqi Kou, Zhanfeng Shen, Yihan Zhang, Haoyu Wang, Pengfei Ji, Lan Huang, Chi Zhang, Yubo Ma

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Automatic detection of fake reviews at marketplaces using expert-based features and consumers’ reactions by A. N. Borodulina, E. V. Mikhalkova

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What Helps to Detect What? Explainable AI and Multisensor Fusion for Semantic Segmentation of Simultaneous Crop and Land Cover Land Use Delineation by Saman Ebrahimi, Saurav Kumar

Subjects: “…Deep learning…”
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Do Spatial Trajectories of Social Media Users Imply the Credibility of the Users’ Tweets During Earthquake Crisis Management? by Ayse Giz Gulnerman

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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription by Manaf Zargoush, Somayeh Ghazalbash, Mahsa Madani Hosseini, Farrokh Alemi, Dan Perri

“…The framework is designed as a data-driven CDSS to determine the best medication strategies based on individual patient profiles, including demographics, comorbidities, and medications. …”
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Effective Epileptic Seizure Detection with Hybrid Feature Selection and SMOTE-Based Data Balancing Using SVM Classifier by Hany F. Atlam, Gbenga Ebenezer Aderibigbe, Muhammad Shahroz Nadeem

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ADVANCED CLUSTERING TECHNIQUES FOR TIN DEPOSIT CLASSIFICATION IN MALAYSIA: A MACHINE LEARNING APPROACH by Meisam Saleki, Kamran Mostafaei, Zakaria Bin Endut, Mohammad Nabi Kianpour

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Struggles of highly educated adult immigrants as identity negotiations through Finnish language learning by Paulina Beatriz Chavez Rodriguez, Ulpukka Isopahkala-Bouret, Tero Järvinen

“…Using narrative positioning analysis, we explore the ways in which highly educated adult immigrants in Finland negotiate their identities through Finnish language learning. …”
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Evidence Based Gait Analysis Interpretation Tools (EB-GAIT) treatment recommendation and outcome prediction models to support decision-making based on clinical gait analysis data. by Michael H Schwartz, Andrew G Georgiadis

“…This paper introduces Evidence-Based Gait Analysis Interpretation Tools (EB-GAIT), a novel framework leveraging machine learning to support treatment decisions. …”
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A dataset dedicated to the training of large- language models for agronomic management practices and production in Norwegian agricultureGithubKaggle by Olena Bugaiova, Kristian Nikolai Jæger Hansen

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Exploring a cost-effective way for nutrient management with machine learning for container plants by Ping Yu, Kuan Qin

“…Due to the close relationships between plant canopy color values and growth parameters, an RGB imaging system using ImageJ to process the image analysis with a supervised machine learning approach was used to classify fertilization input status with four levels: extreme-underuse, sub-underuse, sufficient, and overuse. …”
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