Development and validation of a small-sample machine learning model to predict 5–year overall survival in patients with hepatocellular carcinoma by Tingting Jiang, Xingyu Liu, Wencan He, Hepei Li, Xiang Yan, Qian Yu, Shanjun Mao

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A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation by Zhen-nan Yuan, Yu-juan Xue, Hai-jun Wang, Shi-ning Qu, Chu-lin Huang, Hao Wang, Hao Zhang, Min-ze Zhang, Xue-zhong Xing

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Predictive factors and risk model for depression in patients with type 2 diabetes mellitus: a comprehensive analysis of comorbidities and clinical indicators by Chengzheng Duan, Cheng Luo, Weifeng Jiang, Hui Xu, Yexing Chen, Shiyu Xu, Xiaofang Zhang, Xiaoli Chen, Dongjuan He

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A predictive model for heart failure with preserved ejection fraction following acute myocardial infarction: the HFpEF-AMI score by Xi Wu, Mingxing Wu, Haobo Huang, Zhe Liu, He Huang, Lei Wang

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Development, validation, and visualization of a web-based nomogram for predicting chronic kidney disease incidence at health examination centers by Chuxuan Luo, Lanjun Fu, Lin Liu, Maosheng Chen, Kunliang Chen, Yiwen Li, Bo Lin, Juan Jin, Bin Zhu, Qiang He, Lina Shao

“…We further developed a web-based calculator for convenient application (https://luochuxuan.shinyapps.io/dynnomapp/).Conclusion Our web-based nomogram accurately predicted CKD risks in Chinese health individuals and can be easily used in clinical settings.…”
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Comparing the trends of cancer burden attributed to high BMI in China and globally from 1990 to 2021, with multi-model prediction to 2036 by Nanting Chen, Nanting Chen, Lingzhi Xing, Lingzhi Xing, Fengyun Xiang, Fengyun Xiang, Chengmiao Li, Chengmiao Li, Letai Li, Jingsong Cheng, Yangfan Yu, Yubowen Gong, Xiao Liu, Fangjiao Xie, Ling Chen

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Development and validation of models for predicting preterm birth and gestational latency following emergency cervical cerclage: A multicenter cohort study by Cheng Chen, Baihui Zhao, Yu Pan, Lujiao Chen, Xiaofu Yang, Min Lv, Liping Qiu, Mengmeng Yang, Xia Ying, Minmin Wang, Huanhuan Wang, Xiuying Chen, Xiaoxing Zhang, Xiaofeng Weng, Qiong Luo

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Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study by Shuyu Wen, Chao Zhang, Junwei Zhang, Ying Zhou, Yin Xu, Minghui Xie, Jinchi Zhang, Zhu Zeng, Long Wu, Weihua Qiao, Xingjian Hu, Xingjian Hu, Nianguo Dong, Nianguo Dong

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Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study by Jianying Liu, Mengxiao Jiang, Xiaoping Chen, Yonglan Ge, Zongxin Zheng, Xia Yang, Wenhao Zhou, Huiting Zhang, Meichun Zheng, Baojia Luo

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Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach by Cai C, Zhu H, Li B, Tang C, Ren Y, Guo Y, Li J, Wang L, Li D, Li D

“…We evaluated 147 machine learning models to establish the optimal predictive model. …”
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Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment by Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M

“…Age (Adjusted OR [aOR], 1.08; 95% CI: 1.03– 1.13; p = 0.002), subarachnoid hemorrhage volume (aOR, 1.02; 95% CI: 1.00– 1.05; p = 0.033), World Federation of Neurosurgical Societies grade (WFNS) (aOR, 2.03; 95% CI: 1.05– 3.93; p = 0.035), and Hunt-Hess grade (aOR, 2.36; 95% CI: 1.13– 4.93; p = 0.022) were identified as the independent risk factors of the poor outcome. Then, the prediction models developed have revealed that LightGBM algorithm has a superior performance with an AUC-ROC value of 0.842 in the validation cohort, while the SHAP results showed that age is the most important risk factor affecting functional outcomes.Conclusion: The LightGBM model holds immense potential in facilitating risk stratification for poor-grade aSAH patients undergoing endovascular treatment who are at risk of adverse outcomes, thereby enhancing clinical decision-making processes.Trial Registration: PROSAH-MPC. …”
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The impact of de novo lipogenesis on predicting survival and clinical therapy: an exploration based on a multigene prognostic model in hepatocellular carcinoma by Xin Zhou, Guangzu Cui, Erya Hu, Xinwen Wang, Diya Tang, Xiangyang Zhang, Jiayao Ma, Yin Li, Haicong Liu, Qingping Peng, Ying Han, Yihong Chen, Shan Zeng, Yan Zhang, Hong Shen

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The significance of long chain non-coding RNA signature genes in the diagnosis and management of sepsis patients, and the development of a prediction model by Yong Bai, Jing Gao, Yuwen Yan, Xu Zhao

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Machine learning-based prediction of 6-month functional recovery in hypertensive cerebral hemorrhage: insights from XGBoost and SHAP analysis by Menghui He, Zhongsheng Lu, Yiwei Lv, Zihai Cheng, Qiang Zhang, Xiaoqing Jin, Pei Han

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A time-dependent predictive model for cardiocerebral vascular events in chronic hemodialysis patients: insights from a prospective study by Haowen Zhong, Haowen Zhong, Mengbi Zhang, Mengbi Zhang, Yingye Xie, Yingye Xie, Yuqin Qin, Na Xie, Na Xie, Yuqiu Ye, Yuqiu Ye, Heng Li, Heng Li, Hongquan Peng, Xun Liu, Xiaoyan Su, Xiaoyan Su, Shaohong Li, Shaohong Li

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A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models by Yawei Yang, Junjie Tang, Liping Ma, Feng Wu, Xiaoqing Guan

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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

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Development and Validation of Imaging‐Free Myocardial Fibrosis Prediction Models, Association with Outcomes, and Sample Size Estimation for Phase 3 Trials by Nicholas Black, Joshua Bradley, Gavin Lewis, Jakub Lagan, Christopher Orsborne, Fardad Soltani, John P. Farrant, Theresa McDonagh, Matthias Schmitt, João L. Cavalcante, Martin Ugander, Javed Butler, Mark C. Petrie, Christopher A. Miller, Erik B. Schelbert

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Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer by Yuwen Liang, Haonan Xu, Jie Lin, Wenqiang Tang, Xinlan Liu, Kunyuan Gan, Qiaodi Wan, Xiaobo Du

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Contrast-enhanced ultrasound radiomics model for predicting axillary lymph node metastasis and prognosis in breast cancer: a multicenter study by Shi Yu Li, Yue Ming Li, Yong Qi Fang, Zhi Ying Jin, Jun Kang Li, Xiao Meng Zou, Si Si Huang, Rui Lan Niu, Nai Qing Fu, Yu Hong Shao, Xuan Tong Gong, Mao Ran Li, Wei Wang, Zhi Li Wang

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