Development and validation of an ensemble learning risk model for sepsis after abdominal surgery by Xin Shu, Yujie Li, Yiziting Zhu, Zhiyong Yang, Xiang Liu, Xiaoyan Hu, Chunyong Yang, Lei Zhao, Tao Zhu, Yuwen Chen, Bin Yi

“…Routine clinical variables were implemented for model development. The Boruta algorithm was applied for feature selection. …”
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Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach by Chandra Wadde, Gayatri Routhu, Mark Clemente-Arenas, Surya Prakash Gummadi, Rupesh Kumar

“…The CNN model achieved high accuracy, with precision and recall values exceeding 98% across most categories, demonstrating the robustness and reliability of the model. …”
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Large language models in the management of chronic ocular diseases: a scoping review by Jiatong Zhang, Xiaoxi Song, Bocheng Tian, Mingke Tian, Zhichang Zhang, Jing Wang, Ting Fan

“…Future directions emphasize the need for specialized model training, multimodal algorithm optimization, the establishment of a multinational multicenter clinical validation platform, and the construction of an ethical framework for dynamic regulation. …”
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Development and validation of a carotid plaque risk prediction model for coal miners by Yi-Chun Li, Yi-Chun Li, Yi-Chun Li, Tie-Ru Zhang, Tie-Ru Zhang, Tie-Ru Zhang, Fan Zhang, Fan Zhang, Fan Zhang, Chao-Qun Cui, Chao-Qun Cui, Chao-Qun Cui, Yu-Tong Yang, Yu-Tong Yang, Yu-Tong Yang, Jian-Guang Hao, Jian-Ru Wang, Jiao Wu, Hai-Wang Gao, Ying-Bo Liu, Ming-Zhong Luo, Li-Jian Lei, Li-Jian Lei, Li-Jian Lei

“…The features were initially screened using extreme gradient boosting (XGBoost), random forest, and LASSO regression, and the model was subsequently constructed using logistic regression. …”
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Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review by Maxime Sasseville, Steven Ouellet, Caroline Rhéaume, Malek Sahlia, Vincent Couture, Philippe Després, Jean-Sébastien Paquette, David Darmon, Frédéric Bergeron, Marie-Pierre Gagnon

“…However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations. ConclusionsThe results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm’s preprocessing stage. …”
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Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning by FENG Xin, LIU Yanju, TONG Hongfu, QIAN Shuni

“…Combining with the random forest algorithm, this paper constructs a new meteorological drought forecasting model through regression analysis to screen global SST areas of forecasting significance and takes the Pearl River Basin as an example for application tests. …”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Three widely applicable machine learning algorithms (Random Forests, Gradient Boosting Machine, and XGBoost) were used to screen the metrics, and then the corresponding predictive models were constructed using discriminative analysis, and the best performing model was selected as the target model. …”
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Analysis of the molecular subtypes and prognostic models of anoikis-related genes in colorectal cancer by Lei Shen, Kang Hou, Jifeng Zhang, Xiaodong Li

“…Additionally, various computational algorithms were employed to evaluate the immunotherapeutic responses of different risk groups. …”
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Prognosis model of patients with breast cancer based on metabolism-related LncRNAs by Dan Zhang, Shiwei Ma, Liling Yang, Hongyuan Liu, Han Jiang, Yan Wang

“…Finally, based on the analysis of the CIBERSORT algorithm, lncRNAs used in the construction of the model had a strong correlation with CD8+T cells, activated CD4+T cells and the polarization of M2 macrophages. …”
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Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

“…Objectives: The aim of this study was to compare the performance of 4 algorithms for ATTR-CM detection in a heart failure population and assess the risk for harms due to model bias. …”
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Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study by Shengyu Liu, Anran Wang, Xiaolei Xiu, Ming Zhong, Sizhu Wu

“…The macrofactors affecting model performance were also screened using the multilevel factor elimination algorithm. …”
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Integrating machine learning and multi-omics analysis to unveil key programmed cell death patterns and immunotherapy targets in kidney renal clear cell carcinoma by Fanyan Ou, Yi Pan, Qiuli Chen, Lixiong Zeng, Kanglai Wei, Delin Liu, Qian Guo, Liquan Zhou, Jie Yang

“…We utilized a combination of 101 machine learning algorithms to analyze the TCGA-KIRC cohort and the GSE22541 KIRC patients, screening for cell death patterns closely associated with prognosis from 18 potential modes. …”
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A risk prediction model for gastric cancer based on endoscopic atrophy classification by Yadi Lan, Weijia Sun, Shen Zhong, Qianqian Xu, Yining Xue, Zhaoyu Liu, Lei Shi, Bing Han, Tianyu Zhai, Mingyue Liu, Yujing Sun, Hongwei Xu

“…We employed the Least absolute shrinkage and selection operator (LASSO) to screen variables for the LR model. However, we chose all the variables to construct the models for other machine learning algorithms. …”
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Prediction of postpartum depression in women: development and validation of multiple machine learning models by Weijing Qi, Yongjian Wang, Yipeng Wang, Sha Huang, Cong Li, Haoyu Jin, Jinfan Zuo, Xuefei Cui, Ziqi Wei, Qing Guo, Jie Hu

“…Seven feature selection methods and six ML algorithms were employed to develop models, and their prediction performances were compared. …”
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Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images by Shishir Shetty, Meliz Yuvali, Ilker Ozsahin, Saad Al-Bayatti, Sangeetha Narasimhan, Mohammed Alsaegh, Hiba Al-Daghestani, Raghavendra Shetty, Renita Castelino, Leena R David, Dilber Uzun Ozsahin

“…The highest precision (86.8%) and recall (92.5%) was observed with the SVM model. Conclusion: The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. …”
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A Credible Monitoring Model for Carbon Emissions in Industrial Parks Based on Blockchain Technology by Dong WANG, Jingli FENG, Da LI, Jingwei NIU, Jun LI

“…Finally, LOF algorithm is used to detect long-period abnormal outliers of index data, which can solve the problem of data distortion or self-screening of misstatement to some extent.…”
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Breast mass lesion area detection method based on an improved YOLOv8 model by Yihua Lan, Yingjie Lv, Jiashu Xu, Yingqi Zhang, Yanhong Zhang

“…The YOLOv8-GHOST model incorporates GHOSTConv and C3GHOST modules into the original YOLOv8 model to capture richer feature information while using only 57% of the number of parameters required by the original model. …”
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A multi-feature fusion exercise recommendation model based on knowledge tracing machines by ZHUGE Bin, WANG Ying, XIAO Mengfan, YAN Lei, WANG Bingyan, DONG Ligang, JIANG Xian

“…To address these issues, combining the knowledge tracing machine and the user-based collaborative filtering algorithm, as a KTM-based multi-feature fusion exercise recommendation model, SKT-MFER was proposed. …”
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Cuproptosis-related lncRNA predicts prognosis and immune pathways in osteosarcoma patients by LIAO Jun, FENG Yanbin, XI Deshuang, ZONG Shaohui

“…Objective To identify cuproptosis-related lncRNAs (CRLs) and use them to construct models to predict survival in osteosarcoma (OS) patients. …”
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Enhancing Daylight and Energy Efficiency in Hot Climate Regions with a Perforated Shading System Using a Hybrid Approach Considering Different Case Studies by Basma Gaber, Changhong Zhan, Xueying Han, Mohamed Omar, Guanghao Li

“…A hybrid approach integrating parametric modeling, machine learning, multi-criteria decision-making (MCDM), and genetic algorithm (GA) is used to optimize the design incorporating architects’ preferences. …”
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