Machine learning-driven model for predicting knowledge, attitudes, and practices regarding medication safety among residents in Hubei, China by Chao Mei, Chao Mei, San-Lan Wu, San-Lan Wu, Tao Zhou, Tao Zhou, Yong-Ning Lv, Yong-Ning Lv, Yu Zhang, Yu Zhang, Chen Shi, Chen Shi, Wei-Jing Gong, Wei-Jing Gong

“…Responses were scored systematically. Univariate and multivariate Logistic regression analyses, along with machine learning (ML) techniques, were applied to identify risk factors associated with medication safety KAP.ResultsOut of 1,065 distributed questionnaires, 1,042 were valid (91.8% response rate). …”
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Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator. by Lang Gao, Guang Dong Wang, Xing Yi Yang, Shi Jun Tong, Xu Jie Wang, Yun Ruo Chen, Jin Ying Bai, Ya Xin Zhang

“…This study aimed to develop and validate an interpretable machine learning model for early prediction of SAD in critically ill patients. …”
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Cultivar Differentiation and Origin Tracing of <i>Panax quinquefolius</i> Using Machine Learning Model-DrivenComparative Metabolomics by Rongrong Zhou, Yikun Wang, Lanping Zhen, Bingbing Shen, Hongping Long, Luqi Huang

“…A potential ginsenosides marker panel was used to construct five machine learning models to assist in diagnosing the metabolic phenotypes of American ginseng. …”
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Machine learning-based model for CD4+ conventional T cell genes to predict survival and immune responses in colorectal cancer by Zijing Wang, Zhanyuan Sun, Hengyi Lv, Wenjun Wu, Hai Li, Tao Jiang

“…Building upon this, 101 machine learning algorithms were employed to devise a novel risk assessment framework, which underwent rigorous validation using Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent ROC curves, nomograms, and calibration plots. …”
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Machine learning-based detoxification enzymes-related genes prognosis model in breast cancer: immune landscape and clinical significance by Jingdi Zhang, Wendi Zhan, Haihong Hu, Hongxia Zhu, Bo Hao, Siyu Wang, Zhuo Li, Zhiming Zhang, Taolan Zhang

“…Lasso cox regression analysis and univariate and multivariate Cox analysis were used to process the data, and machine learning algorithm was used to construct breast cancer prognosis model. …”
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Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm by Qianmei Yang, Cuifang Liu, Yongyue Wang, Guifang Dong, Jinghuan Sun

“…Abstract Purpose The aim of this study was to develop and validate a machine learning (ML) based prediction model for sentinel lymph node metastasis in breast cancer to identify patients with a high risk of sentinel lymph node metastasis. …”
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Comparative evaluation of machine learning models for extreme river water level forecasting in Bangladesh: Implications for flood and drought resilience by Md Touhidul Islam, Sujan Chandra Roy, Nusrat Jahan, Al-Mahmud, Md Mazharul Islam, Abdullah Al Ferdaus, Kazunori Fujisawa, A.K.M. Adham

“…This study compares nine machine learning (ML) models for predicting monthly maximum and minimum water levels at three key stations along the Old Brahmaputra River using a 34-year dataset (1990–2024). …”
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Development and validation of interpretable machine learning models to predict distant metastasis and prognosis of muscle-invasive bladder cancer patients by Qian Deng, Shan Li, Yuxiang Zhang, Yuanyuan Jia, Yanhui Yang

“…We sought to develop machine learning (ML) models to predict metastasis and prognosis in MIBC patients. …”
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Prognostic model for log odds of negative lymph node in locally advanced rectal cancer via interpretable machine learning by Ye Wang, Zhen Pan, Huajun Cai, Shoufeng Li, Ying Huang, Jinfu Zhuang, Xing Liu, Guoxian Guan

“…The study included 820 LARC patients who received nCRT between September 2010 and October 2017. Univariate and multivariate Cox regression analyses identified prognostic factors, which were then used to develop risk assessment models with 9 machine learning algorithms. …”
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Construction and Validation of a Model for Predicting Fear of Childbirth: A Cross-Sectional Population Study via Machine Learning by Zhang ZL, Chen KJ, Chen H, Zhu MM, Gu JJ, Jiang LS, Zheng L, Zhou SG

“…Using univariate logistic regression and multivariate logistic regression to analyze the risk factors associated with the occurrence of FOC, we constructed a FOC risk predictive model via ten different machine learning methods and evaluated the predictive performance of the model.Results: Our study indicated that educational level, history of adverse pregnancy outcomes, history of cesarean section, planned pregnancy, assisted reproduction, income, payment, SAS scores, and age are independent risk factors for FOC. …”
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Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities by Yayuan Xia, Linhui Li, Peipei Liu, Tianxu Zhai, Yibing Shi

“…The clinical model was built by identifying clinical risk factors through univariate and multivariate logistic regression analyses. …”
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Univariate and multivariate analyses were used to determine the best clinical characteristics for constructing a clinical model. …”
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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study by Xiaoqin Chen, Zhitong Li, Xiaoying Fan, Yuanyuan Yan, Shiwei Liu

“…Early prediction of carotid atherosclerosis using machine learning models could help in timely intervention and improved patient outcomes for this specific population. …”
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Sarcopenia prediction model based on machine learning and SHAP values for community-based older adults with cardiovascular disease in China by Peil Yu, Xinxin Zhang, Guoxuan Sun, Ping Zeng, Ping Zeng, Ping Zeng, Chu Zheng, Chu Zheng, Chu Zheng, Ke Wang, Ke Wang, Ke Wang

“…Subsequently, we built four machine learning (ML) models to predict SP. After 100 iterations, we selected the best performing model for risk stratification by comparing model discrimination and calibration. …”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…Through differential expression analysis as well as Weighted Gene Co-expression Network Analysis (WGCNA), we identified dysregulated mitotic catastrophe-associated genes, followed by univariate cox regression as well as ten machine learning algorithms to construct robust prognostic models. …”
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Machine learning projection of climate and technology impacts on crops key to food security by Dan Li, Vassili Kitsios, David Newth, Terence John O’Kane

“…Here we introduce a multivariate autoregressive econometrics model that includes a time-varying non-linear variable to account for the decreasing impact of technology on crop yields. …”
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Machine learning based predictive model of the risk of Tourette syndrome with SHAP value interpretation: a retrospective observational study by Aimin Li, Yueying Liu, Yufan Luo, Xue Xiao, Wei Xiao, Ruijin Xie, Xianhui Deng, Zhe Chen, Qian Zhou, Yue Gong, Zhen Chen, Hua Xu

“…Feature selection was conducted using Boruta and multivariable logistic regression analyses, and model construction was undertaken employing 9 distinct machine learning algorithms. 10 distinct features were selected for machine learning algorithm development, and our results indicated that the Gradient Boosting Machine algorithm is the optimal model. …”
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The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review by Yuchen Ma, Mohan Li, Huiqun Wu

“…We also followed the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) guidelines to ensure transparency of ML models included. …”
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Predicting the risk of postoperative gastrointestinal bleeding in patients with Type A aortic dissection based on an interpretable machine learning model by Lin Li, Xing Yang, Wei Guo, Wenxian Wu, Meixia Guo, Huanhuan Li, Xueyan Wang, Siyu Che

“…Predictors were screened using LASSO regression, and four ML algorithms—Random Forest (RF), K-nearest neighbor (KNN), Support Vector Machines (SVM), and Decision Tree (DT)—were employed to construct models for predicting postoperative GIB risk. …”
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A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality. by Brian Critelli, Amier Hassan, Ila Lahooti, Lydia Noh, Jun Sung Park, Kathleen Tong, Ali Lahooti, Nathan Matzko, Jan Niklas Adams, Lukas Liss, Justin Quion, David Restrepo, Melica Nikahd, Stacey Culp, Adam Lacy-Hulbert, Cate Speake, James Buxbaum, Jason Bischof, Cemal Yazici, Anna Evans-Phillips, Sophie Terp, Alexandra Weissman, Darwin Conwell, Philip Hart, Mitchell Ramsey, Somashekar Krishna, Samuel Han, Erica Park, Raj Shah, Venkata Akshintala, John A Windsor, Nikhil K Mull, Georgios Papachristou, Leo Anthony Celi, Peter Lee

“…However, such a prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. …”
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