AI prediction model for endovascular treatment of vertebrobasilar occlusion with atrial fibrillation by Zhi-Xin Huang, Andrea M. Alexandre, Alessandro Pedicelli, Xuying He, Quanlong Hong, Yongkun Li, Ping Chen, Qiankun Cai, Aldobrando Broccolini, Luca Scarcia, Serena Abruzzese, Carlo Cirelli, Mauro Bergui, Andrea Romi, Erwah Kalsoum, Giulia Frauenfelder, Grzegorz Meder, Simona Scalise, Maria Porzia Ganimede, Luigi Bellini, Bruno Del Sette, Francesco Arba, Susanna Sammali, Andrea Salcuni, Sergio Lucio Vinci, Giacomo Cester, Luisa Roveri, Xianjun Huang, Wen Sun

“…While 45.1% achieved favorable outcomes at 90 days, our advanced machine learning approach unveiled subtle interaction effects among clinical variables not captured by traditional statistical methods. …”
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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

“…Methods based on machine learning, and deep learning, rely on a large number of training samples, which is time-consuming and laborious. …”
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Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topso... by Calogero Schillaci, Simone Scarpa, Felipe Yunta, Aldo Lipani, Fernando Visconti, Gábor Szatmári, Kitti Balog, Triven Koganti, Mogens Greve, Giulia Bondi, Georgios Kargas, Paraskevi Londra, Fuat Kaya, Giuseppe Lo Papa, Panos Panagos, Luca Montanarella, Arwyn Jones

“…In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. …”
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Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study. by Guiyou Yang, Tünde Montgomery-Csobán, Wessel Ganzevoort, Sanne J Gordijn, Kimberley Kavanagh, Paul Murray, Laura A Magee, Henk Groen, Peter von Dadelszen

“…Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML] model, and the logistic regression-based fullPIERS model) accurately identify individuals at greatest or least risk of adverse maternal outcomes within 48 h following admission. …”
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Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy by Luca Mastrantoni, Giovanna Garufi, Giulia Giordano, Noemi Maliziola, Elena Di Monte, Giorgia Arcuri, Valentina Frescura, Angelachiara Rotondi, Armando Orlandi, Luisa Carbognin, Antonella Palazzo, Federica Miglietta, Letizia Pontolillo, Alessandra Fabi, Lorenzo Gerratana, Sergio Pannunzio, Ida Paris, Sara Pilotto, Fabio Marazzi, Antonio Franco, Gianluca Franceschini, Maria Vittoria Dieci, Roberta Mazzeo, Fabio Puglisi, Valentina Guarneri, Michele Milella, Giovanni Scambia, Diana Giannarelli, Giampaolo Tortora, Emilio Bria

“…We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). …”
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Fatty Acids of Erythrocyte Membranes and Blood Serum in Differential Diagnosis of Inflammatory Bowel Diseases by M. V. Kruchinina, I. O. Svetlova, M. F. Osipenko, N. V. Abaltusova, A. A. Gromov, M. V. Shashkov, A. S. Sokolova, I. N. Yakovina, A. V. Borisova

“…The study of FA levels in groups with different nosological forms of IBDs using complex statistical analysis, including machine learning methods, made it possible to create diagnostic models that differentiate CD, UC and UCC in the acute stage with high accuracy. …”
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Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study by Suresh K Bhavnani, Weibin Zhang, Daniel Bao, Mukaila Raji, Veronica Ajewole, Rodney Hunter, Yong-Fang Kuo, Susanne Schmidt, Monique R Pappadis, Elise Smith, Alex Bokov, Timothy Reistetter, Shyam Visweswaran, Brian Downer

“…However, the high degree of systematic missingness requires repeating the analysis as the data become more complete by using our generalizable and scalable machine learning code available on the All of Us workbench.…”
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Establishing a radiomics model using contrast-enhanced ultrasound for preoperative prediction of neoplastic gallbladder polyps exceeding 10 mm by Dong Jiang, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Zhenmeng Wang, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…This model, derived from machine learning frameworks including Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGBoost) with fivefold cross-validation, showed AUCs of 0.95 (95% CI: 0.90–0.99) and 0.87 (95% CI: 0.72–1.0) in internal validation. …”
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Modeling Portfolio Optimization based on behavioral Preferences and Investor’s Memory by vahideh mousavi kakhki, Sanaz Khatabi

“…Additionally, researchers can investigate the application of other optimization techniques, such as machine learning algorithms, to portfolio optimization.…”
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Adaptive anomaly detection disruption prediction starting from first discharge on tokamak by X.K. Ai, W. Zheng, M. Zhang, Y.H. Ding, D.L. Chen, Z.Y. Chen, B.H. Guo, C.S. Shen, N.C. Wang, Z.J. Yang, Z.P. Chen, Y. Pan, B. Shen, B.J. Xiao, J-TEXT team

“…While current data-driven machine learning methods perform well in disruption prediction, they require extensive discharge data for model training. …”
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Gaps in U.S. livestock data are a barrier to effective environmental and disease management by Rebecca Logsdon Muenich, Sanskriti Aryal, Amanda J Ashworth, Michelle L Bell, Melanie R Boudreau, Stephanie A Cunningham, K Colton Flynn, Kerry A Hamilton, Ting Liu, Michael L Mashtare, Natalie G Nelson, Barira Rashid, Arghajeet Saha, Danica Schaffer-Smith, Callie Showalter, Aureliane Tchamdja, Jada Thompson

“…We then feature some recent work to improve livestock data availability through remote-sensing and machine learning, ending with our takeaways to address these data needs for the future of environmental and public health management.…”
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Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019 by Jenberu Mekurianew Kelkay, Deje Sendek Anteneh, Henok Dessie Wubneh, Abraham Dessie Gessesse, Gebeyehu Fassil Gebeyehu, Kalkidan Kassahun Aweke, Mikiyas Birhanu Ejigu, Mathias Amare Sendeku, Kirubel Adrissie Barkneh, Hasset Girma Demissie, Wubshet D. Negash, Birku Getie Mihret

“…The dataset was then imported into a Jupyter notebook for further detailed analysis and visualization. An ensemble Machin learning algorithm using different classification models were implemented. …”
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