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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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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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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Visual detection of screen defects in occlusion and missing scenes by YIN Dongfu, DU Mingchen, HU Tianhao, LI Youming, ZHANG Xiaohong, YU Fei Richard

“…The YOLOv8n model is used to detect the position of mobile phone screens in images. …”
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OPTIMIZATION DESIGN OF THE INDEPENDENT WHEEL AXLE-AXLE BASED ON OSF AND RSM by YE Shuang, LIU Qiang, HUANG KunLan, FAN LiZhong, WANG Jie

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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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Artificial Intelligence–Enabled ECG Screening for LVSD in LBBB by Hak Seung Lee, MD, Sooyeon Lee, MD, Sora Kang, MS, Ga In Han, MS, Ah-Hyun Yoo, MS, Jong-Hwan Jang, PhD, Yong-Yeon Jo, PhD, Jeong Min Son, MD, Min Sung Lee, MD, MS, Joon-myoung Kwon, MD, MS, Kyung-Hee Kim, MD, PhD

“…Although artificial intelligence (AI)–driven ECG analysis shows promise for LVSD screening, it remains unclear if a general AI-ECG model or one tailored for LBBB patients yields better performance. …”
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A predictive model of cognitive impairment in Parkinson's disease based on multivariate logistic regression by BA Mengru, YIN Xiaohong, LI Shaoyuan

“…First, the least absolute shrinkage and selection operator (LASSO) algorithm was applied to analyze the risk factors that may affect the cognitive ability of patients, and the clinical variables with high correlation were screened out. …”
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Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network by Xiaorong Li, Lijun Zhang, Dongchen Li, Dan Guo

“…In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in-depth analysis of HR strategic decision-making and an HR strategic decision model is constructed for simulation. …”
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Comprehensive quality assessment of 296 sweetpotato core germplasm in China: A quantitative and qualitative analysis by Chaochen Tang, Yi Xu, Rong Zhang, Xueying Mo, Bingzhi Jiang, Zhangying Wang

“…Near-infrared spectroscopy, combined with a random forest algorithm, enabled rapid screening of superior germplasm, achieving prediction accuracies of 97 % for stem tips and 98 % for roots. …”
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Risk prediction model for overall survival in lung cancer based on inflammatory and nutritional markers by Hongqi Zhou, Weiyun Jin, Lindi Li, Xiangwen Nie, Weiwei Wu, Ran Chen, Qizhen Xie, Haixia Wu, Weiwei Jiang, Min Tang, Jinhai Wang, Maoyuan Wang

“…All patients were followed until death or a uniform administrative censoring point.LASSO logistic regression was employed to model the outcome as a binary classification (death within 1 year: yes/no).This study employed a small-sample modeling approach, initially using LASSO regression for feature selection and dimensionality reduction, followed by variance inflation factor and collinearity screening for secondary feature selection. …”
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Comparing machine learning models for osteoporosis prediction in Tibetan middle aged and elderly women by Peng Wang, Qiang Yin, Kangzhi Ding, Huaichang Zhong, Qundi Jia, Zhasang Xiao, Hai Xiong

“…In test set, the order of AUC from highest to lowest is XGB (0.848), regression (0.801), Random Forest (0.772), SVM (0.755), OSTA (0.739), ANN (0.732). SVM and XGB algorithm models had better screening effect on osteoporosis than OSTA in middle-aged and elderly Tibetan residents in Tibet. …”
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Discovering New Tyrosinase Inhibitors by Using In Silico Modelling, Molecular Docking, and Molecular Dynamics by Kevin A. OréMaldonado, Sebastián A. Cuesta, José R. Mora, Marcos A. Loroño, José L. Paz

“…<b>Methods</b>: Four machine learning algorithms and topographical descriptors were tested for model construction. …”
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External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model by Simone Zappalà, PhD, Francesca Alfieri, MS, Andrea Ancona, PhD, Antonio M. Dell’Anna, MD, Kianoush B. Kashani, MD, MS

“…The performance of the PersEA model, a boosted tree algorithm fed by hourly patient data via electronic health records to provide real-time psAKI predictions, was evaluated using specific metrics that penalize late alarms. …”
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Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin by Pei Zhang, Qiong Chen, Jiahui Lao, Juan Shi, Jia Cao, Xiao Li, Xin Huang

“…Univariate analyses and the least absolute shrinkage and selection operator algorithm were used to screen risk factors and construct the model. …”
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