A machine learning-based model for predicting recurrence in intermediate- and high-risk differentiated thyroid cancer: insights from a retrospective single-center study of 2388 pat... by Yi Li, Zimei Tang, Anwen Ren, Gang Tian, Jianing Zhang, Yiran Wang, Jie Liu, Jie Ming

“…Predictive factors were identified using univariate and multivariate analyses. Six machine learning models were trained and validated, with performance evaluated through accuracy, area under the curve, and clinical utility via decision curve analysis.ResultsIndependent risk factors for recurrence included intraglandular dissemination, total tumor size, bilateral cervical lymph node involvement, and Hashimoto’s thyroiditis, while normal/elevated TSH and multifocal nodules were protective. …”
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Prediction model of axillary lymph node status using an automated breast volume ultrasound radiomics nomogram in early breast cancer with negative axillary ultrasound by Qianqing Ma, Qianqing Ma, Junli Wang, Zhengzheng Tu, Jingwen She, Jianhui Zhu, Feng Jiang, Chaoxue Zhang

“…Select the best classifier from 3 machine learning techniques to build Model 1and radiomics-score (RS). …”
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Developing and validating a machine learning-based model for predicting in-hospital mortality among ICU-admitted heart failure patients: A study utilizing the MIMIC-III database by De Su, Jie Zheng, Yue-kai Shao, Jun-ya Liu, Xin-xin Liu, Kun Yu, Bang-hai Feng, Hong Mei, Song Qin

“…Background Although the assessment of in-hospital mortality risk among heart failure patients in the intensive care unit (ICU) is crucial for clinical decision-making, there is currently a lack of comprehensive models accurately predicting their prognosis. Machine learning techniques offer a powerful means to identify potential risk factors and predict outcomes within multivariable clinical data. …”
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Development and validation of a novel public prediction platform for deciduous caries in preschool children: an observational study from Northwest China by Zhidong Zhang, Zhihao Dou, Xu Zhang, Di Wang, Kamenjiang Abudoureheman, Yimin Yan, Fenyan Kang, Wei Zhang, Wenbo Meng, Kailiang Zhang, Zhige Li, Jie Zhang, Bin Liu, Baoping Zhang

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Mean limiting pressure factors determination in contiguous pile walls using RAFELA and nonlinear regression models in spatially random soil by Divesh Ranjan Kumar, Sittha Kaorapapong, Warit Wipulanusat, Suraparb Keawsawasvong

“…Two nonlinear regression models, multivariate adaptive regression splines (MARS) and the group method of data handling (GMDH), are developed to forecast the mean limiting pressure factor. …”
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Characterizing Duodenal Immune Microenvironment in Functional Dyspepsia: An AutoML-Driven Diagnostic Framework by Zhang X, Fan X, Hu X, Qian Z, Li J, Wu W, Chen L, Wu S, Ma L, Yang C, Zhang T, Su X, Wei W

“…The top 20 critical genes were selected using maximal clique centrality (MCC), and a diagnostic model was developed using LASSO regression and multivariate logistic regression. …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…Clinical predictors were identified through univariate and multivariate logistic regression, which were subsequently integrated with radiomics features to develop combined models. …”
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Association between the postoperative glycemic variability and mortality after craniotomy: a retrospective cohort study and development of a mortality prediction model by Yuanshuo Ge, Guangdong Wang, Yun Huang, Yaxin Zhang

“…A Random Survival Forest (RSF) model was developed using machine learning and interpreted with SHAP values.ResultsHigher GV, as reflected by both elevated CV and rMSSD, was independently associated with increased 28-day and 90-day mortality (CV per 10-unit HR: 1.20; rMSSD per 10-unit HR: 1.02; all P < 0.01). …”
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Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data by Austin A. Barr, Joshua Quan, Eddie Guo, Emre Sezgin, Emre Sezgin

“…BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. …”
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Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National J... by Léonie Hofstetter, Nathalie Schweyckart, Christof Seiler, Christian Brand, Laura C. Rosella, Mazda Farshad, Milo A. Puhan, Cesar A. Hincapié

“…Development of the models will be informed by the updated Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD + AI) statement. …”
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A comprehensive comparison of bias correction methods in climate model simulations: Application on ERA5-Land across different temporal resolutions by Pranav Dhawan, Daniele Dalla Torre, Majid Niazkar, Konstantinos Kaffas, Michele Larcher, Maurizio Righetti, Andrea Menapace

“…Here, we propose a comprehensive analysis of statistical univariate and multivariate, as well as machine learning methods for bias correction, which are compared on different temporal scales, ranging from hourly time steps to monthly aggregations, in an environment of complex Alpine orthography, using ERA5-Land reanalysis data. …”
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Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 199... by Kuijie Zhang, Xiaodong Ma, Xicheng Zhou, Gang Qiu, Chunjuan Zhang

“…All six inflammatory markers were significantly higher in the deceased group (p < 0.001). Weighted multivariable logistic regression showed these markers’ elevated levels significantly correlated with increased ACM risk in hypertensive AR patients across all models (p < 0.001). …”
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Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability by Deyan Liu, Yuge Tian, Min Liu, Shangjian Yang

“…LASSO regression, combined with univariable and multivariable logistic regression, was employed to select feature variables for predictive modeling. …”
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Establishing a novel model to assess exercise capacity in chronic heart failure based on stress echocardiography by Huan Cen, Sinan Chen, Shen Feng, Xiankun Chen, Huiying Zhu, Wei Jiang, Han Zhang, Hongmei Liu, Bo Liu, Weihui Lu, Pengtao Sun

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Predictive potential of cardiovascular risk factors and their associations with arterial stiffness in people of European and Korean ethnic groups by T. A. Brodskaya, V. A. Nevzorova, K. I. Shakhgeldyan, B. I. Geltser, D. A. Vrazhnov, Yu. V. Kistenev

“…Developed using modern machine learning technologies, the assessment aortic PWV models taking into account the ethnic factor can be a useful tool for processing and analyzing data in predictive studies.…”
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Prediction of pancreatic fistula after pancreatoduodenectomy using machine learning by V. A. Suvorov, S. I. Panin, N. V. Kovalenko, V. V. Zhavoronkova, M. P. Postolov, S. E. Tolstopyatov, A. E. Bublikov, A. V. Panova, V. O. Popova

“…The data of 90 (70.3 %) patients were used to train the neural network, and 38 (29.7 %) were used to test the predictive model. In multivariate analysis, the predictors of PF were a comorbidity level above 7 points on the age-adjusted Charlson scale, a diameter of the main pancreatic duct less than 3 mm, and a soft pancreatic consistency. …”
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360 Using machine learning to analyze voice and detect aspiration by Cyril Varghese, Jianwei Zhang, Sara A. Charney, Abdelmohaymin Abdalla, Stacy Holyfield, Adam Brown, Hunter Stearns, Michelle Higgins, Julie Liss, Nan Zhang, David G. Lott, Victor E. Ortega, Visar Berisha

“…Supervised machine learning using five folds cross-validated neural additive network modelling (NAM) was performed on the phonations of aspirator versus non-aspirators. …”
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Regularized regression outperforms trees for predicting cognitive function in the Health and Retirement Study by Kyle Masato Ishikawa, Deborah Taira, Joseph Keaweʻaimoku Kaholokula, Matthew Uechi, James Davis, Eunjung Lim

“…In contrast, tree-based models, such as random forest or boosted trees, are often preferred in machine learning (ML) and commercial settings due to their strong predictive performance. …”
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Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits by F. Shokor, P. Croiseau, H. Gangloff, R. Saintilan, T. Tribout, T. Mary-Huard, B.C.D. Cuyabano

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Predicting Scientific Research Impacts in Biotechnology by Machine Learning Algorithms by Ghasem Azadi Ahmadabadi

“…In this research, Pearson's correlation coefficient and the R software package were used to examine the relationships between the studied indicators. Machine learning algorithms, including multiple linear regression, nearest neighbors, decision trees, random forests, and gradient boosting, were applied and evaluated as predictive models. …”
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