Disproportionality analysis of upadacitinib-related adverse events in inflammatory bowel disease using the FDA adverse event reporting system by Shiyi Wang, Xiaojian Wang, Jing Ding, Xudong Zhang, Hongmei Zhu, Yihong Fan, Changbo Sun

“…This study evaluates upadacitinib-related adverse events (AEs) utilizing data from the US Food and Drug Administration Adverse Event Reporting System (FAERS).MethodsWe employed disproportionality analyses, including the proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) algorithms to identify signals of upadacitinib-associated AEs for treating inflammatory bowel disease (IBD).ResultsFrom a total of 7,037,004 adverse event reports sourced from the FAERS database, 37,822 identified upadacitinib as the primary suspect drug in adverse drug events (ADEs), including 1,917 reports specifically related to the treatment of inflammatory bowel disease (IBD). …”
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Correcting forest aboveground biomass biases by incorporating independent canopy height retrieval with conventional machine learning models using GEDI and ICESat-2 data by Biao Zhang, Zhichao Wang, Tiantian Ma, Zhihao Wang, Hao Li, Wenxu Ji, Mingyang He, Ao Jiao, Zhongke Feng

“…In contrast to advances focused on the refinement of ML algorithms, this study aims to enhance AGB estimation accuracy by integrating an additional Canopy Height (CH) information. …”
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Trends and Gaps in Digital Precision Hypertension Management: Scoping Review by Namuun Clifford, Rachel Tunis, Adetimilehin Ariyo, Haoxiang Yu, Hyekyun Rhee, Kavita Radhakrishnan

“…The most commonly used digital technologies were mobile phones (33/46, 72%), blood pressure monitors (18/46, 39%), and machine learning algorithms (11/46, 24%). In total, 45% (21/46) of the studies either did not report race or ethnicity data (14/46, 30%) or partially reported this information (7/46, 15%). …”
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Tantangan dan Strategi Pencegahan Konflik akibat Intoleransi dan Radikalisme di Era Digital untuk Mewujudkan Keamanan Nasional by Budi Setiawan, Bayu Setiawan, Eri R Hidayat, Pujo Widodo, Herlina Juni Risma Saragih, Achmed Sukendro

“…The results show that the main challenges in conflict prevention include anonymity and accessibility of technology, social media algorithms that support confirmation bias, lack of digital literacy, weak supervision and regulation, and the appeal of radical ideologies. …”
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Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh by Ashikur Rahman, Md. Habibur Rahman

“…The Chi-square test and recursive feature elimination (RFE) are used to find the relevant risk factors of child mortality among the number of factors. Six ML-based algorithms were implemented for predicting child mortality, such as Naïve Bayes, Classification and Regression Trees, Random Forest, C5.0 Classification, Gradient Boosting Machine, and Logistic Regression. …”
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Genetic Biomarkers and Circulating White Blood Cells in Osteoarthritis: A Bioinformatics and Mendelian Randomization Analysis by Yimin Pan, Xiaoshun Sun, Jun Tan, Chao Deng, Changwu Wu, Georg Osterhoff, Nikolas Schopow

“…The bioinformatics methods utilized include the Limma package, WGCNA, PPI network analysis, and machine learning algorithms. Genetic variants were used as instrumental variables to evaluate the potential causal impact of circulating white blood cell (WBC) counts on OA. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…Results Three differentially expressed ARGs (CDC25C, E2F1 and RBL2) with prognostic value were identified by the intersection of multiple machine learning algorithms. A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. …”
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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…This study uses an ensemble learning technique based on multiple machine learning algorithms to effectively and precisely monitor the leaf nitrogen content in the tree canopy using multispectral canopy footage of custard apple trees taken via Unmanned Aerial Vehicle (UAV) across different growth phases. …”
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Development of a Predictive Model of Occult Cancer After a Venous Thromboembolism Event Using Machine Learning: The CLOVER Study by Anabel Franco-Moreno, Elena Madroñal-Cerezo, Cristina Lucía de Ancos-Aracil, Ana Isabel Farfán-Sedano, Nuria Muñoz-Rivas, José Bascuñana Morejón-Girón, José Manuel Ruiz-Giardín, Federico Álvarez-Rodríguez, Jesús Prada-Alonso, Yvonne Gala-García, Miguel Ángel Casado-Suela, Ana Bustamante-Fermosel, Nuria Alfaro-Fernández, Juan Torres-Macho

“…XGBoost, LightGBM, and CatBoost algorithms were used to train different prediction models, which were subsequently validated in a hold-out dataset. …”
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Association between estimated glucose disposal rate and cardiovascular diseases in patients with diabetes or prediabetes: a cross-sectional study by Jinhao Liao, Linjie Wang, Lian Duan, Fengying Gong, Huijuan Zhu, Hui Pan, Hongbo Yang

“…Three machine learning methods (SVM-RFE, XGBoost, and Boruta algorithms) were employed to select the most critical variables. …”
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Early risk assessment in paediatric and adult household contacts of confirmed tuberculosis cases by novel diagnostic tests (ERASE-TB): protocol for a prospective, non-interventiona... by Ursula Panzner, Katharina Kranzer, Tsitsi Bandason, Kuda Mutasa, Sandra Rukobo, Charles Sandy, Bariki Mtafya, Andrea Rachow, Norbert Heinrich, Michael Hoelscher, Judith Bruchfeld, Olena Ivanova, Nyanda Elias Ntinginya, Doreen Pamba, Laura Olbrich, Issa Sabi, Simeon Mwanyonga, Elmar Saathoff, Willyhelmina Olomi, Junior Mutsvangwa, Hazel M Dockrell, Edson Tawanda Marambire, Denise Banze, Alfred Mfinanga, Theodora D Mbunda, Khosa Celso, Gunilla Kallenius, Claire J Calderwood, Christof Geldmacher, Kathrin Held, Tejaswi Appalarowthu, Friedrich Rieß, Anna Shepherd, Christopher Sundling, Mishelle Mugava, Martha Chipinduro, Lwitiho Sudi, Antelmo Haule, Emmanuel Sichone, Paschal Qwaray, Harrieth Mwambola, Lilian Minja, Peter Edwin, Dogo Ngalison, Stella Luswema, Celina Nhamuave, António Machiana, Carla Madeira, Emelva Manhiça, Nádia Sitoe, Jorge Ribeiro

“…The Early Risk Assessment in TB Contacts by new diagnoStic tEsts (ERASE-TB) study aims to evaluate novel diagnostics and testing algorithms for early TB diagnosis and accurate prediction of disease progression among household contacts (HHCs) exposed to confirmed index cases in Mozambique, Tanzania and Zimbabwe.Methods and analysis A total of 2100 HHCs (aged ≥10 years) of adults with microbiologically-confirmed pulmonary TB will be recruited and followed up at 6-month intervals for 18–24 months. …”
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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks by Chalita Jainonthee, Phutsadee Sanwisate, Panneepa Sivapirunthep, Chanporn Chaosap, Raktham Mektrirat, Sudarat Chadsuthi, Veerasak Punyapornwithaya

“…This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). …”
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Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale by Minghan Cheng, Xiuliang Jin, Chenwei Nie, Kaihua Liu, Tianao Wu, Yuping Lv, Shuaibing Liu, Xun Yu, Yi Bai, Yadong Liu, Lin Meng, Xiao Jia, Yuan Liu, Lili Zhou, Fei Nan

“…However, most previous studies have relied on remote sensing data from one or a few periods for yield estimation, thus lacking a comprehensive description of entire crop growth. Furthermore, past algorithms have not considered their applicability across different observational scales (e.g., unmanned aerial vehicle (UAV)- and satellite-observed). …”
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Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models by Li Zhang, Xiaodong Gao, Shuyi Zhou, Zhibo Zhang, Tianjie Zhao, Yaohui Cai, Xining Zhao

“…These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. …”
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Monitoring poultry social dynamics using colored tags: Avian visual perception, behavioral effects, and artificial intelligence precision by Florencia B. Rossi, Nicola Rossi, Gabriel Orso, Lucas Barberis, Raul H. Marin, Jackelyn M. Kembro

“…However, maintaining the identity of individuals over time, especially in homogeneous poultry flocks, remains challenging for algorithms. We propose using differentially colored “backpack” tags (black, gray, white, orange, red, purple, and green) detectable with computer vision (eg. …”
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Dynamic selectout and voting-based federated learning for enhanced medical image analysis by Saeed Iqbal, Adnan N Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Atif Mahmood, Saaidal Razalli Bin Azzuhri

“…The voting system and the dynamic SelectOut algorithms improve the convergence of the FL model and successfully handle the difficulties presented by uneven and heterogeneous datasets. …”
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A multidimensional assessment of adverse events associated with paliperidone palmitate: a real-world pharmacovigilance study using the FAERS and JADER databases by Siyu Lou, Zhiwei Cui, Yingyong Ou, Junyou Chen, Linmei Zhou, Ruizhen Zhao, Chengyu Zhu, Li Wang, Zhu Wu, Fan Zou

“…Results A total of 27,672 ADE reports related to paliperidone palmitate were identified in FAERS, with 285 significantly disproportionate preferred terms (PTs) identified by all four algorithms. Paliperidone palmitate-associated ADEs encompassed 27 System Organ Classes (SOCs). …”
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. …”
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Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review by Claire R. van Genugten, Claire R. van Genugten, Melissa S. Y. Thong, Melissa S. Y. Thong, Melissa S. Y. Thong, Wouter van Ballegooijen, Wouter van Ballegooijen, Wouter van Ballegooijen, Annet M. Kleiboer, Annet M. Kleiboer, Donna Spruijt-Metz, Arnout C. Smit, Mirjam A. G. Sprangers, Mirjam A. G. Sprangers, Yannik Terhorst, Yannik Terhorst, Heleen Riper, Heleen Riper, Heleen Riper

“…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …”
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Home-Based Intervention Tool for Cardiac Telerehabilitation: Protocol for a Controlled Trial by Francesca Mastorci, Maria Francesca Lodovica Lazzeri, Lamia Ait-Ali, Paolo Marcheschi, Paola Quadrelli, Massimiliano Mariani, Rafik Margaryan, Wanda Pennè, Marco Savino, Giuseppe Prencipe, Alina Sirbu, Paolo Ferragina, Corrado Priami, Alessandro Tommasi, Cesare Zavattari, Pierluigi Festa, Stefano Dalmiani, Alessandro Pingitore

“…The secondary aims are to implement the system in a “real-life” context of postcardiac surgical rehabilitation, and to create a data set and a data collection methodology to prototype data analytics algorithms and artificial intelligence techniques for customizing the rehabilitation pathway. …”
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