Construction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model by Chunyu Lei, Anhui Fu, Bin Li, Shengfu Zhou, Jun Liu, Yu Cao, Bo Zhou

“…These variables were consistent with the results of logistic multivariate analysis.ConclusionsThe application of improved machine learning models for the analysis of patient clinical data can effectively predict the risk of poor prognosis following endovascular intervention for intracranial aneurysms at an early stage. …”
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Development and validation of machine learning models for predicting acute kidney injury in acute-on-chronic liver failure: a multimodel comparative study by Jing Zhang, Shuxuan Tang, Jingyuan Liu, Ang Li

“…Therefore, this study aimed to develop prediction models for AKI in ACLF patients based on machine learning (ML) algorithms.Methods This retrospective study enrolled 1,076 adult patients diagnosed with ACLF, with AKI defined according to the International Club of Ascites criteria. …”
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Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques by Yang Li, Kun Zou, Yixuan Wang, Yucheng Zhang, Jingtao Zhong, Wu Zhou, Fang Tang, Lu Peng, Xusheng Liu, Lili Deng

“…The present study use dataset from the Chinese Health and Retirement Longitudinal Study (CHARLS) and utilizes advanced Gradient Boosting algorithms to develop predictive models. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the key predictors, and multivariate logistic regression was utilized to validate the independent predictive power of the variables. …”
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Prediction of heat transfer in a rotor-stator cavity cooled by multiple jets: Integration of CFD models and machine learning for performance optimization by Abdellatif EL hannaoui, Rachid Boutarfa

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Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters by Jianchen Pu, Yimin Yao, Xiaochun Wang

“…The calibration curve revealed good agreement between the actual and predicted probabilities, whereas the decision curve showed the significant clinical application of the model. Additionally, the AUC of the model in the external independent test cohort was 0.945.DiscussionWe used an online predictive tool to develop a predictive machine-learning model. …”
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Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors by Yuk Yee Chong, Chun Man Lawrence Lau, Tianshu Jiang, Chunyi Wen, Jiang Zhang, Amy Cheung, Michelle Hilda Luk, Ka Chun Thomas Leung, Man Hong Cheung, Henry Fu, Kwong Yuen Chiu, Ping Keung Chan

“…Six of them were selected after univariate and multivariate analysis. Five machine learning models were trained with stratified 10-fold cross-validation and assessed by discrimination and calibration analysis to determine the optimal predictive model. …”
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MRI-based radiomics machine learning model to differentiate non-clear cell renal cell carcinoma from benign renal tumors by Ruiting Wang, Lianting Zhong, Pingyi Zhu, Xianpan Pan, Lei Chen, Jianjun Zhou, Yuqin Ding

“…Among the 14 machine learning classification models constructed, the combined model with LR has the highest efficiency in differentiating non-ccRCC from benign renal tumors. …”
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Development and external validation of a machine learning model for cardiac valve calcification early screening in dialysis patients: a multicenter study by Xiaoxu Wang, Yinfang Li, Zixin Cao, Yunuo Li, Jingyuan Cao, Yao Wang, Min Li, Jing Zheng, Siqi Peng, Wen Shi, Qianqian Wu, Junlan Yang, Yaping Fang, Aiqing Zhang, Xiaoliang Zhang, Bin Wang

“…Predictive factors were selected using LASSO regression combined with univariate and multivariate analyses. Machine learning models including CatBoost, XGBoost, decision tree, support vector machine, random forest, and logistic regression were used to develop the CVC risk model. …”
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Leveraging Subjective Parameters and Biomarkers in Machine Learning Models: The Feasibility of <i>lnc-IL7R</i> for Managing Emphysema Progression by Tzu-Tao Chen, Tzu-Yu Cheng, I-Jung Liu, Shu-Chuan Ho, Kang-Yun Lee, Huei-Tyng Huang, Po-Hao Feng, Kuan-Yuan Chen, Ching-Shan Luo, Chien-Hua Tseng, Yueh-His Chen, Arnab Majumdar, Cheng-Yu Tsai, Sheng-Ming Wu

“…Associations with emphysema severity, defined by a low attenuation area percentage (LAA%) threshold of 15%, were evaluated using simple and multivariate-adjusted models. The dataset was then split into training and validation (80%) and test (20%) subsets. …”
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Risk prediction of hyperuricemia based on particle swarm fusion machine learning solely dependent on routine blood tests by Min Fang, Chengjie Pan, Xiaoyi Yu, Wenjuan Li, Ben Wang, Huajian Zhou, Zhenying Xu, Genyuan Yang

“…Subsequently, a risk prediction model is constructed based on the parameter optimization of five machine learning models using the PSO algorithm. …”
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Genetic Links Between Common Lung Diseases and Lung Cancer Progression: Bioinformatics and Machine Learning Insights by Md Ali Hossain, Tania Akter Asa, Md. Zulfiker Mahmud, AKM Azad, Mohammad Zahidur Rahman, Mohammad Ali Moni, Ahmed Moustafa

“…Integrated mRNA-Seq and clinical data were analyzed via univariate and multivariate Cox Proportional Hazard models to elucidate the influence of significant genes on survival. …”
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Development and Validation of a Machine Learning Prediction Model for Textbook Outcome in Liver Surgery: Results From a Multicenter, International Cohort by Jane Wang, MD, Amir Ashraf Ganjouei, MD, MPH, Taizo Hibi, MD, PhD, Nuria Lluis, MD, PhD, Camilla Gomes, MD, Fernanda Romero-Hernandez, MD, Han Yin, BA, Lucia Calthorpe, MD, Yukiyasu Okamura, MD, PhD, Yuta Abe, MD, PhD, Shogo Tanaka, MD, PhD, Minoru Tanabe, MD, PhD, Zeniche Morise, MD, PhD, Horacio Asbun, MD, PhD, David Geller, MD, Mohammed Abu Hilal, MD, PhD, Mohamed Adam, MD, Adnan Alseidi, MD, EdM, International Hepatectomy Study Group, Alison Baskin, Annie Wong-On-Wing, Annie Yang, Devesh Sharma, Taisuke Imamura, Masanori Nakamura, Yuya Miura, Koki Hayashi, Masatsugu Ishii, Keita Shimata, Kazuya Hirukawa, Hiroki Ueda, June S. Peng, Lucas Thornblade, Kenzo Hirose, Kimberly Kirkwood, Eric Nakakura, Carlos Corvera, Ajay Maker

“…This study aimed to (1) develop a machine learning (ML) model that predicts the textbook outcome in liver surgery (TOLS) using preoperative variables and (2) validate the TOLS criteria by determining whether TOLS is associated with long-term survival after hepatectomy. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…Pore pressure is used as the output level to generate data-driven models. 70% of the dataset is used for training the machine learning models, while the remaining 30% is reserved for testing the models to evaluate their performance and generalization capability. …”
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Predicting the prognosis of epithelial ovarian cancer patients based on deep learning models by Zihan Li, Jiao Wang, Yixin Zhang, Zhen Yang, Fanchen Zhou, Xueting Bai, Qian Zhang, Wenchong Zhen, Rongxuan Xu, Wei Wu, Zhihan Yao, Xiaofeng Li, Yiming Yang

“…Evaluation of several models based on multiple metrics including C-index, ROC curve, calibration curve and decision curve analysis (DCA).ResultsThrough univariate and multivariate COX proportional risk regression analyses, we selected 12 significantly independent prognostic factors affecting overall survival (P&lt;0.05). …”
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Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study by Pan Li, Chuanqi Zhong, Xianjun Huang, Zhi Cai, Tianhong Guo

“…The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to establish a predictive model. …”
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Optimizing Decision Making on Business Processes Using a Combination of Process Mining, Job Shop, and Multivariate Resource Clustering by Hanung Nindito Prasetyo, Riyanarto Sarno, Dedy Rahman Wijaya, Raden Budiraharjo, Indra Waspada, Kelly Rossa Sungkono, Abdullah Faqih Septiyanto

“…In the context of optimizing business processes with a process mining approach, most current process models are optimized with a trace clustering approach to explore the model and to perform analysis on the resulting process model. …”
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A multivariate cell-based liquid biopsy for lung nodule risk stratification: Analytical validation and early clinical evaluation by Jason D. Berndt, Fergal J. Duffy, Mark D. D'Ascenzo, Leslie R. Miller, Yijun Qi, G. Adam Whitney, Samuel A. Danziger, Anil Vachani, Pierre P. Massion, Stephen A. Deppen, Robert J. Lipshutz, John D. Aitchison, Jennifer J. Smith

“…In the platform, standardized cells are exposed to small volumes of patient serum, and the resulting transcriptomic response is analyzed using machine learning tools to develop disease classifiers. …”
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Prediction Model for Familial Aggregated HBV‐Associated Hepatocellular Carcinoma Based on Serum Biomarkers by Linmei Zhong, Guole Nie, Qiaoping Wu, Honglong Zhang, Haiping Wang, Jun Yan

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Sustainable approach of strength measurement for soil’s stabilized with geo-polymer with hybrid ensemble models by Ishwor Thapa, Sufyan Ghani, Nishant Kumar, Megha Gupta, Sunil Saharan, Prabhu Paramasivam, Abinet Gosaye Ayanie

“…Five machine learning models Random Forest, Support Vector Regression, Extreme Learning Machine, Artificial Neural Networks, and Multivariate Adaptive Regression Splines were developed and combined in a unique hybrid ensemble. …”
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Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients. by Chaoqun Huang, Shangzhi Shu, Miaomiao Zhou, Zhenming Sun, Shuyan Li

“…This study aimed to construct and validate an interpretable predictive model of LAT/SEC risk in NVAF patients using machine learning (ML) methods.…”
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