Simultaneous non-contrast assessment of cardiac microstructure and perfusion in vivo in the human heart by Camila Munoz, Eunji Lim, Pedro F. Ferreira, Dudley J. Pennell, Sonia Nielles-Vallespin, Andrew D. Scott

“…A simulation study was performed to investigate the optimal fitting algorithms for the IVIM parameters, which was subsequently used to create pixel-wise IVIM parameter maps for the in vivo acquisitions. …”
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The use of patient-generated health data in the management of low anterior resection syndrome: a qualitative study by Olivia Monton, Olivia Monton, Allister Smith, Sarah Sabboobeh, Marie Demian, Julie Cornish, Steven D. Wexner, Peter Christensen, Peter Christensen, Amandeep Ghuman, Liliana G. Bordeianou, Liliana G. Bordeianou, Liliana G. Bordeianou, Celia Keane, Celia Keane, Syed Husain, Alessandra Gasior, Alessandra Gasior, Natalie Leon, Julie Savard, Lieba R. Savitt, Margit Majgaard, Gitte Kjær Sørensen, Melanie Mills, Fateme Rajabiyazdi, Marylise Boutros, Marylise Boutros

“…Finally, some participants saw promise in leveraging PGHD to inform the creation of automated treatment algorithms for LARS management.ConclusionsThis study highlights many gaps in the processes of patient-generated LARS data collection and review. …”
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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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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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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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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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Predicting positive Clostridioides difficile test results using large-scale longitudinal data of demographics and medication history by Anh Pham, Robert El-Kareh, Frank Myers, Lucila Ohno-Machado, Tsung-Ting Kuo

“…Blanket testing at admission is in general not recommended, and current predictive models either used moderate sample sizes, over-inflated the number of covariates, or chose non-interpretable algorithms. We aim to develop models using patient data to predict positive Clostridioides difficile test results with discrimination performance, interpretable results, and a reasonable number of covariates that reflect health over a long-time span. …”
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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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Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.ObjectivesThis study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. …”
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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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The association of origin and environmental conditions with performance in professional IRONMAN triathletes by Beat Knechtle, Mabliny Thuany, David Valero, Elias Villiger, Pantelis T. Nikolaidis, Marilia S. Andrade, Ivan Cuk, Thomas Rosemann, Katja Weiss

“…Three different ML models were built and evaluated, based on three algorithms, in order of growing complexity and predictive power: Decision Tree Regressor, Random Forest Regressor, and XG Boost Regressor. …”
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Low-cost phone-based LiDAR scanning technology provides sub-centimeter accuracy when measuring the main dimensions of motor-manual tree felling cuts by Stelian Alexandru Borz, Andrea Rosario Proto

“…Short-range LiDAR technology integrated in affordable mobile platforms has already been proved to produce reliable estimates on objects located in a limited space, and point cloud processing algorithms have been developed to compare two instances of the same object, potentially enabling the quantification of tree-level wood loss. …”
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Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
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Recent advances in anion exchange membrane technology for water electrolysis: a review of progress and challenges by Abdul Qayoom, Muhammad Shakeel Ahmad, H. Fayaz, Atika Qazi, Jeyraj Selvaraj, Rahadian Zainul, Krismadinata, Nasrudin Abd Rahim, Farruh Atamurotov, Thien Khanh Tran, Basma Souayeh, Natei Ermias Benti

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Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability by Sangwon Byun, Ah Young Kim, Min-Sup Shin, Hong Jin Jeon, Hong Jin Jeon, Chul-Hyun Cho, Chul-Hyun Cho

“…This study evaluated the feasibility of using machine-learning algorithms to detect stress automatically in MDD and PD patients, as well as healthy controls (HCs), based on HRV features.MethodsThe study included 147 participants (MDD: 41, PD: 47, HC: 59) who visited the laboratory up to five times over 12 weeks. …”
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ϵ-Confidence Approximately Correct (ϵ-CoAC) Learnability and Hyperparameter Selection in Linear Regression Modeling by Soosan Beheshti, Mahdi Shamsi

“…Linear regression modeling is an important category of learning algorithms. The practical uncertainty of the label samples in the training data set has a major effect in the generalization ability of the learned model. …”
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