Identification and predictive machine learning models construction of gut microbiota associated with lymph node metastasis in colorectal cancer by Yongzhi Wu, Chengen Deng, Zigui Huang, Yongqi Huang, Chuanbin Chen, Mingjian Qin, Zhen Wang, Fuhai He, Shenghai Liu, Rumao Zhong, Jun Liu, Chenyan Long, Jungang Liu, Weizhong Tang, Xiaoliang Huang

“…Moreover, machine learning models based on gut microbiota predict LNM status accurately, offering a novel approach for personalized CRC treatment.…”
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Gradient Boosted Trees Predictive Models for Surface Roughness in High-Speed Milling in the Steel and Aluminum Metalworking Industry by Victor Flores, Brian Keith

“…A good alternative to improve the surface quality results in the manufacture of metal parts is using predictive models of the surface roughness. In this document, we present work done with experimental data obtained from two high-speed machining (HSM) machines with different types of tools and cutting conditions, conducted under an experimental design with interest in three of factors commonly studied to generate surface roughness models: tool characteristics, cutting conditions, and characteristics of the machined material. …”
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Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients by Madison Bush, Scott Jones, Catriona Hargrave

“…Due to the limited sample size further training of the predictive models including dMRI anatomy is recommended.…”
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Development and Validation of Predictive Models for Inflammatory Bowel Disease Diagnosis: A Machine Learning and Nomogram-Based Approach by Dong R, Wang Y, Yao H, Chen T, Zhou Q, Zhao B, Xu J

“…Cohorts 1 and 2 were used to create predictive models. The parameters of the machine learning model established by Cohorts 1 and 2 were merged, and nomogram models were developed using Logistic regression. …”
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Predicting purulent meningitis in very preterm infants: a novel clinical model by Xiaowei Sun, Rui Jing, Yang Li

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Study on prediction model and influencing factors of progression-free survival in colorectal cancer by CHEN Jiaying, CHU Yimin, PENG Haixia

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Precision prognostic model for uveal melanoma: nomogram prediction and comparison with AJCC system by Wentao Wu, Jingjing Zhu, Yuzhu Wu, Daning Li, Rong Liu, Yuchen Zheng, Qiuqiong Deng, Yiqiang Yuan

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A predictive model for early neurological deterioration after intravenous thrombolysis in patients with ischemic stroke by Liping He, Liping He, Meng Zhang, Meng Zhang, Fei Xu, Zhangsong Wu, Huijuan Chen, Ying Li, Ran Chen

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An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID‐19 by Zhe Chen, Nicholas W. Russo, Matthew M. Miller, Robert X. Murphy, David B. Burmeister

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Evolution and Predictive Analysis of Spatiotemporal Patterns of Habitat Quality in the Turpan–Hami Basin by Yaqian Li, Yongqiang Liu, Yan Qin, Kun Zhang, Reifat Enwer, Weiping Wang, Shuai Yuan

“…Additionally, the InVEST-PLUS coupling model was employed to forecast habitat conditions under three different scenarios in 2050. …”
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Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies by Nuray Vakitbilir, Abrar Islam, Alwyn Gomez, Kevin Y. Stein, Logan Froese, Tobias Bergmann, Amanjyot Singh Sainbhi, Davis McClarty, Rahul Raj, Frederick A. Zeiler

“…Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. …”
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Auxiliary identification of depression patients using interpretable machine learning models based on heart rate variability: a retrospective study by Min Yang, Huiqin Zhang, Minglan Yu, Yunxuan Xu, Bo Xiang, Xiaopeng Yao

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Construction and validation of a predictive model for the risk of rebleeding in patients with esophageal and gastric varices hemorrhage by Wei Gao, Yu-Shuang Huang, Ying-De Wang

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A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques by Cong Peng, Cheng Gong, Xiaoya Zhang, Duxian Liu

“…Additionally, Shapley Additive Explanations (SHAP) were applied to interpret feature importance within the model, revealing the clinical factors that influence survival predictions.ResultsAmong the nine models, the lightGBM model exhibited the best performance, with an AUC and F1 score of (0.8, 0.809) for 1-year survival prediction, (0.809, 0.751) for 3-year survival prediction, and (0.773, 0.611) for 5-year survival prediction. …”
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Establishment and validation of a predictive model for Lobar pneumonia caused by Mycoplasma pneumoniae infection in children by Yonghan Luo, Houxi Bai, Feng jiao, Yan Guo, Xiaotao Yang, Yanchun Wang

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Construction and Validation of a Predictive Model for Mortality Risk in Patients with Acinetobacter baumannii Bloodstream Infection by Li X, Cai D, Mei C, Huang X

Subjects: “…acinetobacter baumannii﹒bloodstream infection﹒predictive model﹒septic shock…”
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Development and validation of a clinical prediction model for osteoporosis diagnosis by lumbosacral X-ray and radiomics by Xiaofeng Chen, Xiaofeng Chen, Dongling Cai, Dongling Cai, Hao Li, Weijun Guo, Qian Li, Qian Li, Jinjun Liang, Junxian Xie, Jincheng Liu, Zhen Xiang, Wenxuan Dong, Sihong OuYang, Zhuozheng Deng, Qipeng Wei

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Design and Simulation Verification of Model Predictive Attitude Control Based on Feedback Linearization for Quadrotor UAV by Xingyu Yuan, Jinfa Xu, Shengwei Li

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From Data to Diagnosis: Machine Learning Revolutionizes Epidemiological Predictions by Abdul Aziz Abdul Rahman, Gowri Rajasekaran, Rathipriya Ramalingam, Abdelrhman Meero, Dhamodharavadhani Seetharaman

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Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a multicenter study by Zongren Ding, Jianxing Zeng, Guoxu Fang, Pengfei Guo, Weiping Zhou, Yongyi Zeng

“…However, accurately predicting MVI preoperatively remains a challenge. This study aims to evaluate the application of large language models (LLMs), specifically ChatGPT 4o, in predicting MVI in HCC and to compare its performance with traditional clinical models. …”
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