A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…A classification prediction model for the haemoglobin concentration after kidney transplantation was constructed. …”
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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…ObjectiveThis study aimed to formulate a predictive model for assessing the likelihood of readmission among patients diagnosed with heart failure. …”
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VETC predicting model based on CE-CT can predict prognosis and assisting treatment plan for solitary HCC: better together with radiomics by Yu-Meng Zhao, Shuang-Shuang Xie, Jian Wang, Zhan-Dong Hu, Sheng-Juan Yao, Rui-Hang Wang, Jia-Ming Qin, Cheng Zhang, Ya-Min Zhang, Zhao-Xiang Ye, Jian-Hua Yan, Wen Shen

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Dengue Severity Prediction in a Hyperendemic Region in Colombia by Jorge Emilio Salazar Flórez, Katerine Marín Velasquez, Luz Stella Giraldo Cardona, Ángela María Segura Cardona, Berta Nelly Restrepo Jaramillo, Margarita Arboleda

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Interpretability Study of Gradient Information in Individual Travel Prediction by Ziheng Su, Pengfei Zhang, Xiaohui Song, Yifan Li

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Bio-Inspired Mamba for Antibody–Antigen Interaction Prediction by Xuan Liu, Haitao Fu, Yuqing Yang, Jian Zhang

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Modelling plum (Prunus domestica) yield in Norway by A. Døving

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Location prediction algorithm based on movement tendency by Wen LI, Shi-xiong XIA, Feng LIU, Lei ZHANG, Guan YUAN

Subjects: “…location prediction;moving object;Markov model;movement tendency;region reach-ability…”
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Clinical and anamnestic factors of epilepsy development and course in children with cerebral palsy by V. P. Zykov, F. A. Murachueva, N. V. Chebanenko, R. M. Alieva

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Verifying the Performance of Multiple Linear Regression in Predicting the Indicator of Mass Accumulation of Waste. The Case of Lubelskie Voivodship by Tomasz Szul, Krzysztof Nęcka

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Analyzing Momentum Shifts in Tennis: A Machine-Learning Approach to Predicting Match Outcomes by Yuean Xia, Changfeng Li, Tanran Zhang

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A dynamic nomogram predicting nosocomial infections in patients after colon cancer surgery by Xue Yao, Shuhui Wang, Anning Lu, Yun Xu, Na Li

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Research on Mode Shift Control of Multimode Hybrid Systems Based on Hybrid Model Prediction by Xinxin Zhao, Jiadian Liu, Bing Li

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Bayesian Architecture for Predictive Monitoring of Unbalance Faults in a Turbine Rotor–Bearing System by Banalata Bera, Shyh-Chin Huang, Po Ting Lin, Yu-Jen Chiu, Jin-Wei Liang

“…Therefore, monitoring unbalance faults is essential for predictive maintenance. While conventional time-invariant mathematical models can assess the impact of these faults, they often rely on proper assumptions of system factors like bearing stiffness and damping characteristics. …”
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Multivariate Segment Expandable Encoder-Decoder Model for Time Series Forecasting by Yanhong Li, David C. Anastasiu

“…Additionally, MSEED incorporates a simple vanilla encoder-decoder model for strengthening rolling predictions. The framework has been tested on four challenging real-world datasets, focusing on two critical forecasting scenarios: long-term predictions (three days ahead) and rolling predictions (every four hours) to simulate real-time decision-making in water resource management. …”
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Predicting preeclampsia in early pregnancy using clinical and laboratory data via machine learning model by Songchang Chen, Jia Li, Xiao Zhang, Wenqiu Xu, Zhixu Qiu, Siyao Yan, Wenrui Zhao, Zhiguang Zhao, Peirun Tian, Qiang Zhao, Qun Zhang, Weiping Chen, Huahua Li, Xiaohong Ruan, Gefei Xiao, Sufen Zhang, Liqing Hu, Jie Qin, Wuyan Huang, Zhongzhe Li, Shunyao Wang, Rui Zhang, Shang Huang, Xin Wang, Yao Yao, Jian Ran, Danling Cheng, Qi Luo, Teng Pan, Ruyun Gao, Jing Zheng, Yuxuan Wang, Cong Liu, Xianling Cao, Xuanyou Zhou, Naixin Xu, Lanlan Zhang, Xu Han, Haolin Wang, Suihua Feng, Shuyuan Li, Jianguo Zhang, Lijian Zhao, Fengxiang Wei

“…Abstract Background This study was performed to characterize the relationship of various laboratory test indicators with clinical information and Preeclampsia (PE) development. Then, prediction models for early-onset preeclampsia (EOPE), late-onset preeclampsia (LOPE), and preterm preeclampsia (Preterm PE) were developed using maternal characteristics and laboratory data. …”
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Predicting composition of a functional food product using computer simulation by M. A. Nikitina, I. M. Chernukha, M. P. Artamonova, A. T. Qusay

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An explainable AI-based approach for predicting undergraduate students academic performance by Fatema Tuz Johora, Md Nahid Hasan, Aditya Rajbongshi, Md Ashrafuzzaman, Farzana Akter

“…Two eXplainable Artificial Intelligence (XAI) algorithms, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), were integrated to provide a comprehensible prediction of the best model and determine the significant factors. …”
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Development of a nomogram for predicting the risk of carcinoma in chronic atrophic gastritis by Jia-Yi Zhang, Ding Li, Guo-Jie Hu

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Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines by Cheng Yin, Le Thanh Binh, Duong Tran Anh, Son T. Mai, Anh Le, Van-Hau Nguyen, Van-Chien Nguyen, Nguyen Xuan Tinh, Hitoshi Tanaka, Nguyen Trung Viet, Long D. Nguyen, Trung Q. Duong

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