Who benefits from adjuvant chemotherapy? Identification of early recurrence in intrahepatic cholangiocarcinoma patients after curative-intent resection using machine learning algor... by Qi Li, Hengchao Liu, Yubo Ma, Zhenqi Tang, Chen Chen, Dong Zhang, Zhimin Geng

“…The feature importance ranking based on machine learning algorithms showed that AJCC 8th edition N stage, number of tumors, T stage, perineural invasion, and CA125 as the top five variables associated with early recurrence, which was consistent with the independent risk factors of multivariate logistic regression model. …”
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Development of a clinical-radiological nomogram for predicting severe postoperative peritumoral brain edema following intracranial meningioma resection by Chen Bo, Geng Ao, Lu Siyuan, Lu Siyuan, Wu Ting, Wang Dianjun, Zhao Nan, Shan Xiuhong, Deng Yan, Sun Eryi

“…Based on these analyses, we developed five predictive models using R software: conventional logistic regression, XGBoost, random forest, support vector machine (SVM), and k-nearest neighbors (KNN). …”
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Impact of the number of dissected lymph nodes on machine learning-based prediction of postoperative lung cancer recurrence: a single-hospital retrospective cohort study by Kyoichi Okishio, Shinji Atagi, Kensuke Kojima, Hironobu Samejima, Toshiteru Tokunaga, Hyungeun Yoon

“…Five machine learning models were trained using clinicopathological variables to predict postoperative recurrence. …”
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The application and predictive value of the weight-adjusted-waist index in BC prevalence assessment: a comprehensive statistical and machine learning analysis using NHANES data by Wenjing Wang, Biao Wu, Jian Li, Yibiao Shang, Mengting Liu, Qi Fang, Han Zhang, Xiang Li, Dongdi Wu

“…Machine learning models ranked WWI as one of the top predictors, with the random forest model retaining WWI as an important variable, while LASSO excluded it. …”
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Establishment and validation of predictive model of ARDS in critically ill patients by Senhao Wei, Hua Zhang, Hao Li, Chao Li, Ziyuan Shen, Yiyuan Yin, Zhukai Cong, Zhaojin Zeng, Qinggang Ge, Dongfeng Li, Xi Zhu

“…This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. …”
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Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk f... by Xi-Ru Zhang, Wen-Fang Zhong, Rui-Yan Liu, Jie-Lin Huang, Jing-Xiang Fu, Jian Gao, Pei-Dong Zhang, Dan Liu, Zhi-Hao Li, Yan He, Hongwei Zhou, Zhuang Li

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Cardiometabolic index predicts cardiovascular events in aging population: a machine learning-based risk prediction framework from a large-scale longitudinal study by Yuanxi Luo, Yuanxi Luo, Zhiyang Yin, Xin Li, Xin Li, Chong Sheng, Ping Zhang, Dongjin Wang, Dongjin Wang, Yunxing Xue

“…Following baseline characteristic comparisons and CVD incidence rate calculations, we implemented multiple Cox regression models to assess CMI’s cardiovascular risk prediction capabilities. …”
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Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortal... by Yang Chen, Zhenkun Yang, Yang Liu, Yuanjie Li, Ziyi Zhong, Garry McDowell, Coleen Ditchfield, Taipu Guo, Mingjuan Yang, Rui Zhang, Bi Huang, Ying Gue, Gregory Y. H. Lip

“…We aims to explore the relationships between TyG with ICU all-cause mortality and other prognosis, and to develop machine learning (ML) models in predicting ICU all-cause mortality in the first-ever strokes. …”
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Computational Linguistics Applications in AI-Based Investment and Cost Structuring Models by Miralieva Dilafruz, Ramatov Jumaniyoz, Azimova Lola, Khusamiddinova Malika, Alimbaeva Shahlo, Omonova Laylo

“…A regression-based model consisting of semantic, syntactic, and pragmatic dimensions was created and estimated through correlation and multivariate regression analysis. …”
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Development and validation of survival prediction tools in early and late onset colorectal cancer patients by Wanling Li, Jinshan Liu, Yuntong Lan, Dongling Yu, Bingqiang Zhang

“…We conducted univariate and multivariate regression analyses on the training dataset to identify key survival factors and develop predictive machine learning models. …”
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Number of Publications on New Clinical Prediction Models: A Bibliometric Review by Banafsheh Arshi, Laure Wynants, Eline Rijnhart, Kelly Reeve, Laura Elizabeth Cowley, Luc J Smits

“…By taking random samples for each year, we identified eligible studies that developed a multivariable model (ie, diagnostic or prognostic) for individual-level prediction of a health outcome across all medical fields. …”
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Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models by Hajar Fatorachian, Kulwant Pawar

“…Trained on a multi-year sales dataset, the ARIMA model excelled in capturing seasonal patterns, while the MLR model effectively incorporated multivariable factors such as temperature, product type, and promotional activity. …”
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Identification of Diagnostic Biomarkers for Colorectal Polyps Based on Noninvasive Urinary Metabolite Screening and Construction of a Nomogram by Yang Xie, Yiyi Jin, Zide Liu, Jun Li, Qing Tao, Yonghui Wu, Youxiang Chen, Chunyan Zeng

“…Calibration plots and DCA confirmed the model's accuracy and clinical utility. Conclusions This study successfully identified seven urinary metabolites as potential noninvasive biomarkers for CRP. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…The prediction effects of different magnetite water content models were compared, and finally the best model was selected to improve the accuracy of water content detection in mineral processing and smelting. …”
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The association of obesity and lipid-related indicators with all-cause and cardiovascular mortality risks in patients with diabetes or prediabetes: a cross-sectional study based on... by Zhaoqi Yan, Xing Chang, Zhiming Liu, Ruxiu Liu, Xiufan Du

“…Additionally, we compared the predictive performance of eight machine learning (ML) algorithms regarding mortality risk and used the SHAP method to clarify the significance of obesity and lipid-related indicators in mortality prediction.ResultsThe results of the multivariable Cox regression analysis reveal significant associations between TyG, TyG-WWI, and ABSI with all-cause mortality among patients with diabetes/prediabetes. …”
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Exploring the potential associations between single and mixed volatile compounds and preserved ratio impaired spirometry using five different approaches by Chenyuan Deng, Yu Jiang, Yuechun Lin, Hengrui Liang, Wei Wang, Ying Huang, Jianxing He

“…We trained ten machine learning models to identify PRISm and assessed the relative importance of each feature using Shapley Additive Explanations (SHAP). …”
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AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model by Berend J. D. Gort, Godfrey M. Kibalya, Angelos Antonopoulos

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Association between visceral fat accumulation and sarcopenia: A cross-sectional study by Shengwei Wang, Weigen Wu, Ling Zhang, Qi Zeng, Yu Luo, Weiwen He, Wei Chen, Wen He

“…This study aimed to develop nine machine learning (ML) models incorporating visceral fat indicators to predict the risk of sarcopenia, with Shapley Additive Explanations (SHAP) applied to enhance model interpretability. …”
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Exploring the effect of the triglyceride-glucose index on bone metabolism in prepubertal children, a retrospective study: insights from traditional methods and machine-learning-bas... by Shunshun Cao, Aolei Chen, Botian Song, Yangyang Hu

“…Methods In this retrospective study of 332 prepubertal children (163 boys and 169 girls), we used multivariate linear regression and five machine learning (ML) algorithms to explore the association between the TyG index and BTMs, including β-C-terminal telopeptide of type 1 collagen (β-CTx), total procollagen type 1 N-terminal propeptide (T-P1NP), and N-terminal mid-fragment of osteocalcin (N-MID). …”
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DR potential probabilistic forecasting model of load aggregators based on ensemble learning by YEERSEN Sailike, YANG Xi, LI Meiyi, LI Na, GE Xinxin, WANG Fei

“…Firstly, the multivariate influencing features of the DR potential of load aggregators are extracted, and the support vector machine-based recursive feature elimination (SVM-RFE) method is used to select features. …”
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