Influencing factors of sleep disorders in elderly hypertensive patients and the construction of nomogram prediction model by YUAN Xiuying, DAI Xiangyi, LIAO Juan

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Cell Death and Senescence‐Based Molecular Classification and an Individualized Prediction Model for Lung Adenocarcinoma by Pan Wang, Chaoqi Zhang, Peng Wu, Zhihong Zhao, Nan Sun, Qi Xue, Shugeng Gao, Jie He

“…The clinical utility of CDSI was further validated using 9185 pan‐cancer samples, demonstrating the broad relevance of our prediction model across various cancer types and its potential clinical implications for cancer management.…”
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An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations by Baoqian Huan, Xianglong Li, Jianguo Wang, Tao Hu, Zihao Tao

“…On the test set, the model maintained high prediction accuracy, with an R 2 value of 0.9105 and an RMSE of 0.0403. …”
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The Hybrid Model: Prediction-Based Scheduling and Efficient Resource Management in a Serverless Environment by Louai Shiekhani, Hui Wang, Wen Shi, Jiahao Liu, Yuan Qiu, Chunhua Gu, Weichao Ding

“…To address these issues, we propose a multi-level scheduling solution: the Hybrid Model. This model is designed to reduce the frequency of cold starts while maximizing container utilization. …”
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Flow similarity model predicts allometric relationships among Acer platanoides L. branches by Charles A. Price, Gregory A. Dahle, Jason C. Grabosky

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Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers by Hengjun Wan, Huaju Tian, Cheng Wu, Yue Zhao, Daiying Zhang, Yujie Zheng, Yuan Li, Xiaoxia Duan

“…Herein, we constructed a multidimensional postoperative delirium risk‐prediction model incorporating multiple demographic parameters and blood biomarkers to enhance prediction accuracy. …”
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The Prediction of Serum C-Reactive Protein Concentration Using Nonlinear Mixed-Effects Model by Suk Joo Bae, Gyu Ri Kim, Sun Geu Chae, Yeesuk Kim

“…The bi-exponential model with random effects is applied to predict temporal CRP concentrations in patients after hip surgery. …”
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Prediction models for patients with esophageal or gastric cancer: A systematic review and meta-analysis. by H G van den Boorn, E G Engelhardt, J van Kleef, M A G Sprangers, M G H van Oijen, A Abu-Hanna, A H Zwinderman, V M H Coupé, H W M van Laarhoven

“…<h4>Background</h4>Clinical prediction models are increasingly used to predict outcomes such as survival in cancer patients. …”
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Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

“…The results show that the MGCN–Transformer model has a MAE, RMSE, and R² error index of 0.26, 0.42, and 95.93%, respectively, in the multi-step prediction task of parking demand, demonstrating its superior predictive accuracy compared to other benchmark models.…”
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A Prediction Model for External Root Resorption of the Second Molars Associated With Third Molars by Zhengwei Kou, Wuyang Zhang, Chen Li, Yu Zhang, Zijian Song, Yuzhen Zou, Haijing Wang, Zhenghua Liu, Bahetibieke Huerman, Tiange Deng, Kaijin Hu, Yang Xue, Ping Ji

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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…Current machine learning (ML) models demonstrate suboptimal predictive performance in KD treatment response prediction, primarily due to their limited ability to effectively process categorical variables and interpret tabular clinical data. …”
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A blood test-based machine learning model for predicting lung cancer risk by Lihi Schwartz, Naor Matania, Matanel Levi, Teddy Lazebnik, Teddy Lazebnik, Shiri Kushnir, Noga Yosef, Assaf Hoogi, Dekel Shlomi, Dekel Shlomi

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Prediction models after hepatectomy for hepatocellular carcinoma-based ultrasonic radiomics: an observational study by Dong Jiang, Jialun Ren, Yi Qian, Yijun Gu, Ru Wang, Hua Yu, Hui Dong, Dongyu Chen, Yan Chen, Haozheng Jiang, Yiran Li

“…Abstract Background This study aims to develop and validate predictive models for postoperative complications and early recurrence in hepatocellular carcinoma (HCC) patients. …”
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Risk models to predict chronic kidney disease and its progression: a systematic review. by Justin B Echouffo-Tcheugui, Andre P Kengne

“…Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.…”
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A Dual Convolution Model for Grating Lobe Prediction in Directivity of Parametric Array Loudspeakers by Shangming Mei, Yihua Hu, Mohammed Alkahtani, Mohammad Nasr Esfahani

“…Given this potential, understanding sound directivity is the basis for designing a successful steerable parametric array loudspeaker. Current directivity models face challenges in predicting grating lobes due to the neglect of key factors such as transducer spacing. …”
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Inflammation-nutrition biomarker model for survival prediction in lung cancer patients with concurrent tuberculosis by Hongqi Zhou, Zihao Zhao, Jinhai Wang, Weiyun Jin, Bensong Xian, Lindi Li, XiangWen Nie, WeiWei Wu, Ran Chen, QiZhen Xie, HaiXia Wu, WeiWei Jiang, Min Tang, YuXin Li

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Stacked ensemble model for accurate crop yield prediction using machine learning techniques by Ramesh V, Kumaresan P

“…Our work proposes a stacked ensemble model designed for the purpose of predicting crop yield. …”
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Dynamic flood risk prediction in Houston: a multi-model machine learning approach by Shuchi Mishra, Aproorv Bajpai, Agradeep Mohanta, Biplab Banerjee, Shrishti Rajput, Sudipta Kundu

“…In assessing flood susceptibility in Houston, key geographical parameters such as drainage density, slope, distance from rivers and roads, LULC, and rainfall data were analyzed using machine learning models, including Decision Trees, Random Forest, Gradient Boosting, SVM, and ANN. …”
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Predicting Mortality in Elderly Patients with Hypertensive Crises: Development and Validation of a Risk Model by Erkan Boga

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Solar radiation prediction: A multi-model machine learning and deep learning approach by C Vanlalchhuanawmi, Subhasish Deb, Md. Minarul Islam, Taha Selim Ustun

“…A feature selection process using Pearson’s coefficient identified the most relevant inputs, while quantile regression was employed for uncertainty assessment, mean prediction interval, and prediction interval coverage probability models. …”
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