Patterns of childhood trauma co-occurrence and its predictivity for suicidality: A machine learning approach by Wenbang Niu, Yi Feng, Shicun Xu, Xin Cui, Zhihao Ma, Yuanyuan Wang

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Method of tail beam posture prediction of top coal caving hydraulic support based on LSTM by Yupeng YAO, Jinglin ZHANG, Wu XIONG

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Gas concentration prediction in photoacoustic spectroscopy using PSO-EAP-CNN to address correlation degradation by Zhanshang Su, Pengpeng Wang, Zhengzhuo Li, Yawen Li, Tianxiang Zhao, Yujie Duan, Fupeng Wang, Cunguang Zhu

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Prediction of the need for intensive oxygen supplementation during hospitalisation among subjects with COVID-19 admitted to an academic health system in Texas: a retrospective coho... by John W Davis, Beilin Wang, Ewa Tomczak, Chia Chi-Fu, Wissam Harmouch, David Reynoso, Philip Keiser, Miguel Mauricio Cabada

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Study on Photovoltaic Plant Site Selection Models Based on Geographic and Environmental Features by RAO Zhi, YANG Zaimin, YANG Xiongping, LI Jiaming, YANG Ping, WEI Zhichu

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Prognostic correlation analysis of colorectal cancer patients based on monocyte to lymphocyte ratio and folate receptor-positive circulating tumor cells and construction of a machi... by Siying Pan, Chi Lu, Chi Lu, Hongda Lu, Hongda Lu, Hongfeng Zhang

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Development of a Preliminary Screening Tool for Predicting Polycystic Ovarian Syndrome using Machine Learning and Deep Learning Models with Non Invasive Qualitative Features: A Cas... by Hanumanth Narni, Vasudeva Rao Ananthasetty, SD Jilani

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SICNet_season V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data by Y. Ren, Y. Ren, Y. Ren, X. Li, X. Li, X. Li, Y. Wang, Y. Wang, Y. Wang

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Predicting the risk of acute kidney injury in patients with acute pancreatitis complicated by sepsis using a stacked ensemble machine learning model: a retrospective study based on... by Ying Zhou, Fuyuan Li, Zhan Wang, Zhanjin Wang, Ruiling Bian, Zhangtuo Xue, Junjie Cai

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External validation of an artificial intelligence model using clinical variables, including ICD-10 codes, for predicting in-hospital mortality among trauma patients: a multicenter... by Seungseok Lee, Do Wan Kim, Na-eun Oh, Hayeon Lee, Seoyoung Park, Dong Keon Yon, Wu Seong Kang, Jinseok Lee

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Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

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ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure by Bowen Du, Haohan Liang, Yuhang Wang, Junchen Ye, Xuyan Tan, Weizhong Chen

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Integrating artificial intelligence with network evolution theory for community behavior prediction in dynamic complex systems by Yongyan Zhao, Jian Li

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Short-term Wind Speed Prediction Based on Wavelet Packet Decomposition and BP Neural Network by WANG Ning, LUO Rubin, LIAO Jun, LI Jun, JIANG Yi, YANG Zechuan, YUAN Junjie

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HSoMLSDP: A Hybrid Swarm-Optimized Machine Learning Framework for Software Defect Prediction by Madhusmita Das, Biju R. Mohan, Ram Mohana Reddy Guddeti

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Deep learning-based prediction model of acute kidney injury following coronary artery bypass grafting in coronary heart disease patients: a multicenter clinical study from China by Biao Hou, Biao Hou, Tingting Liu, Pengyun Yan, Yuqing Wang, Xuejian Hou, Liang Li, Haiping Yang, Lin Chen, Taoshuai Liu, Kui Zhang, Shijun Xu, Yang Li, Ran Dong

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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

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An artificial intelligence platform for predicting postoperative complications in metastatic spinal surgery: development and validation study by Weihao Jiang, Juan Zhang, Weiqing Shi, Xuyong Cao, Xiongwei Zhao, Bin Zhang, Haikuan Yu, Shengjie Wang, Yong Qin, Mingxing Lei, Yuncen Cao, Boyu Zhu, Yaosheng Liu

“…These variables were thus incorporated as key features in all the predictive models. Among the six machine learning models, the eXGBM model demonstrated the highest prediction accuracy, with an area under the curve (AUC) of 0.924 (95% CI: 0.884–0.965), outperforming the RF model (AUC = 0.892, 95% CI: 0.846–0.938) and the KNN model (AUC = 0.869, 95% CI: 0.818–0.920). …”
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Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma. by Han Wang

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Machine-learning approach to atrial fibrillation prediction among individuals without prior cardiovascular diseases by Niklas Hammar, Javier Louro, Karin Modig, Shunsuke Murata, Mozhu Ding

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