The relationship between clinical subtypes, prognosis, and treatment in ICU patients with acute cholangitis using unsupervised machine learning methods by Yuanhui Sheng, Shan Xu, Shu Zhang, Dan Zhang

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Predictors of Length-of-Stay Among Transcatheter Aortic Valve Replacement Patients Using a Supervised Machine Learning Algorithm by Gregory L. Judson, MD, Jeff Luck, PhD, Skye Lawrence, BA, Rakan Khaki, MPH, Harsh Agrawal, MD, Krishan Soni, MD, Kirsten Tolstrup, MD, Vijayadithyan Jaganathan, MD, Vaikom S. Mahadevan, MD

“…Results: Twenty and 22 variables were identified and included as important predictors for the SLOS and LLOS multivariable models, respectively. The predictive power of both the SLOS (sensitivity 0.81, specificity 0.70, area under the curve [AUC] 0.82) and LLOS (sensitivity 0.45, specificity 0.94, AUC 0.85) ML models were more robust than the standard multivariable model (SLOS AUC 0.65, LLOS AUC 0.65). …”
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Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis by Jinyang Wang, Haonan Shi, Xiaowei Wang, Enhong Dong, Jian Yao, Yonghan Li, Ye Yang, Tingting Wang

“…Stratified analyses assessed confounders and interactions, while the significance of variables was determined using a machine learning model. Renovating the dwelling during the mother’s pregnancy (OR = 1.50; 95% CI 1.15–1.96) was identified as a risk factor for childhood AD. …”
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GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer by Oumeima Thaalbi, Moulay A. Akhloufi

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Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases by Zhiyuan Ning, Xuanfei Jiang, Huan Huang, Honggang Ma, Ji Luo, Xiangyan Yang, Bing Zhang, Ying Liu

“…The multivariate models revealed that age, gender, red blood cell count, race/ethnicity, systolic blood pressure, and total protein level were the top six predictors of NT-proBNP. …”
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Verification of subclinical carotid atherosclerosis as part of risk stratification in overweight and obesity: the role of machine learning in the development of a diagnostic algori... by M. A. Druzhilov, T. Yu. Kuznetsova, D. V. Gavrilov, A. V. Gusev

“…Comparative analysis of mathematical models obtained using multivariate logistic regression (MLR) with stepwise inclusion of predictors and machine learning (ML) for assessing the probability of subclinical carotid atherosclerosis in normotensive overweight and obese patients without cardiovascular diseases and/or diabetes.Material and methods. …”
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Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study by Xinxin Qin, Binxu Qiu, Litao Ge, Song Wu, Yuye Ma, Wei Li

“…We constructed the machine learning model using 10-fold cross-validation. …”
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Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer by Pu Zhou, Pu Zhou, Hongyan Qian, Pengfei Zhu, Jiangyuan Ben, Jiangyuan Ben, Guifang Chen, Qiuyi Chen, Lingli Chen, Jia Chen, Ying He, Ying He

“…We compared 10 ML models based on radiomics features: support vector machine (SVM), logistic regression (LR), random forest, extra trees (ET), naïve Bayes (NB), k-nearest neighbor (KNN), multilayer perceptron (MLP), gradient boosting ML (GBM), light GBM (LGBM), and adaptive boost (AB). …”
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Climate drivers of forest ecosystem services supply in the hilly mountainus regions of southern China based on SHAP-enhanced machine learning by Qi Mengjuan, Guo Luo, Liu Wenshu, Wang Weiyin, Jiang Chunqian, Bai Yanfeng

“…Furthermore, we integrated an interpretable machine learning model, Random Forest–Shapley Additive Explanations (SHAP), to identify principal climatic drivers and characterize their nonlinear impacts on FESs. …”
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Unraveling the significance of cuproptosis in hepatocellular carcinoma heterogeneity and tumor microenvironment through integrated single-cell sequencing and machine learning appro... by Wang Liu, Liangjing Xia, Yuan peng, Qiang Cao, Ke Xu, Huiyan Luo, Yongjun Peng, Yanping Zhang

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Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study by Suling Chen, Suling Chen, Lixia Zhang, Lixia Zhang, Lixia Zhang, Jingchun Mao, Jingchun Mao, Zhe Qian, Yuanhui Jiang, Yuanhui Jiang, Xinrui Gao, Xinrui Gao, Mingzhu Tao, Mingzhu Tao, Guangyu Liang, Guangyu Liang, Jie Peng, Jie Peng, Shaohang Cai, Shaohang Cai

“…We also developed several machine-learning models, assessing their performance using internal and external datasets to generate receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). …”
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A machine learning approach to carbon emissions prediction of the top eleven emitters by 2030 and their prospects for meeting Paris agreement targets by Arju Manara Begum, Mahadee Al Mobin

“…This work represents a novel integration of multivariate machine learning modelling, data-driven feature selection, and policy-oriented emission forecasts, establishing new methodological and empirical benchmarks in climate analytics.…”
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Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma by Ke Ma, Jie Xu, Jie Xu, Congyue Wang, Congyue Wang, Xu Cao, Xu Cao, Wenjie Yu, Jingjing Xi, Xuan Zhang, Jiamin Zhan, Yang Liu, Aoyang Yu, Aoyang Yu, Shuhan Liu, Yanhua Liu, Yanhua Liu, Chong Chen, Chong Chen, Xiaoli Mai, Xiaoli Mai

“…The robustness of the model was assessed using the concordance index (C-index), Kaplan-Meier survival analyses, receiver operating characteristic (ROC) curves, and both univariate and multivariate Cox regression analyses. …”
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Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods by Liyan Jiang, Hongling Wang, Yang Xiao, Linlin Xu, Huoying Chen

“…Analytical methods included multivariable logistic regression, LASSO regression, and five machine learning models: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). …”
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Brain functional connectivity analysis of fMRI-based Alzheimer's disease data by Maitha S. Alarjani, Badar A. Almarri

“…These features are then used as inputs for an Extreme Learning Machine (ELM) model to classify AD stages. The model's performance is assessed through comprehensive evaluation metrics to ensure robustness and reliability. …”
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Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data by Nur Hani Zainal, Hui Han Tan, Ryan Yee Shiun Hong, Michelle Gayle Newman

“…ResultsML models outperformed logistic regression. The multivariable ML models using the 10 most important predictors achieved good performance, with the area under the receiver operating characteristic curve (AU-ROC) values ranging from .71 to .72 at posttreatment and 1MFU. …”
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Sedative exposure and mortality in intracranial hypertensive tuberculous meningitis: a cohort study with propensity-score matching and machine learning analysis by Shijuan Cui, Fazheng Shen, Jianing Liang, Fan Li, Xiangyang Wang, Xin Liu, Haigang Chang

“…Primary outcomes included 200-day mortality assessed using multivariable logistic regression and Cox proportional hazards models. …”
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Oxidative balance score predicts chronic kidney disease risk in overweight adults: a NHANES-based machine learning study by Leying Zhao, Leying Zhao, Cong Zhao, Cong Zhao, Yuchen Fu, Yuchen Fu, Xiaochang Wu, Xiaochang Wu, Xuezhe Wang, Xuezhe Wang, Yaoxian Wang, Yaoxian Wang, Yaoxian Wang, Huijuan Zheng

“…Survey-weighted logistic regression models were used to assess the association between OBS and CKD, with multivariable adjustment. …”
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Association of dietary inflammatory index and vigorous physical activity on phenotypic age acceleration: a cross-sectional study with machine learning by Shuoqi Li, Jianming Zhou, Dandan Zhang, Qiuyu Du

“…The NHANES is designed with a sophisticated, multistage probability sampling methodology and is specifically tailored to comprehensively assess the health and nutritional conditions of the non-institutionalized population. Five machine learning models were constructed to predict participants’ PhenoAgeAccel.ResultsThe PhenoAgeAccel of participants in Groups 3 (anti-inflammatory diet + insufficient VPA) and 4 (anti-inflammatory diet + sufficient VPA) were −2.72 (95% CI − 3.44, −1.93; p < 0.001), and −1.61 (95% CI − 2.65, −0.63; p < 0.001), respectively, when compared to the participants under 60 years old in Group 1 (pro-inflammatory diet + insufficient VPA). …”
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Correlation between Neutrophil-to-Lymphocyte Ratio and Diabetic Neuropathy in Chinese Adults with Type 2 Diabetes Mellitus Using Machine Learning Methods by Lijie Zhu, Yang Liu, Bingyan Zheng, Danmeng Dong, Xiaoyun Xie, Liumei Hu

“…Machine learning methods, XGBoost and SVM, built prediction models, showing that NLR can predict the onset of DPN. …”
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