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

“…Abstract This study aims to develop a multidimensional risk prediction model, identify characteristic inflammation-nutrition biomarkers, and optimize clinical decision-making. …”
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Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica M Veličković, Jacqueline Dinnes

“…<h4>Background</h4>Pressure injuries (PIs) pose a substantial healthcare burden and incur significant costs worldwide. Several risk prediction tools to allow timely implementation of preventive measures and a subsequent reduction in healthcare system burden are available and in use. …”
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MST-YOLO: Small Object Detection Model for Autonomous Driving by Mingjing Li, Xinyang Liu, Shuang Chen, Le Yang, Qingyu Du, Ziqing Han, Junshuai Wang

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Online Core Temperature Estimation for Lithium-Ion Batteries via an Aging-Integrated ECM-1D Coupled Model-Based Algorithm by Yiqi Jia, Lorenzo Brancato, Marco Giglio, Francesco Cadini

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Could chronic Vardenafil administration influence the cardiovascular risk in men with type 2 diabetes mellitus? by Daniele Santi, Michela Locaso, Antonio R Granata, Tommaso Trenti, Laura Roli, Chiara Pacchioni, Vincenzo Rochira, Cesare Carani, Manuela Simoni

“…<h4>Introduction</h4>Appropriate algorithms for the prediction of cardiovascular risk are strongly suggested in clinical practice, although still controversial. …”
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Risk assessment of water inrush from coal floor based on enhanced samples with class distribution by Shiwei Liu, Jiaxin Zhao, Hao Yu, Jiaqi Chen

“…A prediction model of the water inrush risk for the coal seam floor was established using a coupled algorithm of extreme learning machines, self-adaptive differential evolution, and CDMTD (PCA-CDMTD-SaDE-ELM) and was used to evaluate the water inrush risk in the 19,105 working face of the Yunjialing Mine. …”
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A Machine Learning-Based Risk Prediction Model During Pregnancy in Low-Resource Settings by Kapil Tomar, Chandra Mani Sharma, Tanisha Prasad, Vijayaraghavan M. Chariar

“…Sometimes these reasons become a direct cause of maternal mortality as well. Awareness of the risks and early detection may contribute to a reduction in maternal deaths during pregnancy and childbirth. …”
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Development and validation of a risk prediction model for acute kidney injury in coronary artery disease by Ming Ye, Chang Liu, Duo Yang, Hai Gao

“…Abstract Background Acute Kidney Injury (AKI) is a sudden and often reversible condition characterized by rapid kidney function reduction, posing significant risks to coronary artery disease (CAD) patients. …”
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Anthropometric changes and risk of diabetes: are there sex differences? A longitudinal study of Alberta’s Tomorrow Project by Ming Ye, Paula J Robson, Dean T Eurich, Jennifer E Vena, Jian-Yi Xu, Jeffrey A Johnson

“…However, this sex-specific difference of approximately 10% of the total risk associated with anthropometric changes had limited significance. …”
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Vulnerability assessment for reducing risk of environmental hazards based on ecosystem services: Case of Alborz Province by Naser Moghadasi, Sharareh Pourebrahim, Hosein Azarnivand

“…Alborz Province suffers from various risks for which applying environmental services, such as conservation of soil nutrients, biodiversity and diverse vegetation of land, can be effective in their reduction.  …”
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Predictive Analysis of Cardiovascular Disease Risk Factors in Romania using Machine Learning and Medical Statistics by Radu-Anton MOLDOVAN, Sebastian-Aurelian ŞTEFĂNIGĂ

“…To do this, we used machine learning algorithms such as logistic regression, random forests, support vector machines (SVM), and artificial neural networks (ANNs) to forecast cardiovascular risk factors from past medical data and epidemiology trends. …”
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Risk−Based Cost−Benefit Optimization Design for Steel Frame Structures to Resist Progressive Collapse by Feng Guo, Xuancen Li, Aiyan Xu, Yuchen Zhang, Wanping Pan

“…To address this problem, this paper uses risk analysis to optimize the progressive collapse resistance design of steel frame structures. …”
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A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening by Emre Yalçın, Serpil Aslan, Mesut Toğaçar, Süleyman Cansun Demir

“…<b>Background/Objectives:</b> The aim of this study is to develop a hybrid artificial intelligence (AI) approach to improve the accuracy, efficiency, and reliability of Down Syndrome (DS) risk prediction during first trimester prenatal screening. …”
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Interval valued inventory model with different payment strategies for green products under interval valued Grey Wolf optimizer Algorithm fitness function by Subhash Chandra Das, Md. Al-Amin Khan, Ali Akbar Shaikh, Adel Fahad Alrasheedi

“…To boost sales and profitability, a vendor occasionally provides a price reduction in exchange for a down payment. Demanding a down payment from a customer earns interest and carries without any risk of default. …”
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Preprocessing Method for Performance Enhancement in CNN-Based STEMI Detection From 12-Lead ECG by Yeonghyeon Park, Il Dong Yun, Si-Hyuck Kang

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A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease by James L. Jr. Januzzi, Naveed Sattar, Muthiah Vaduganathan, Craig A. Magaret, Rhonda F. Rhyne, Yuxi Liu, Serge Masson, Javed Butler, Michael K. Hansen

“…Conclusions We describe a validated risk algorithm that accurately predicts cardio-kidney outcomes across a broad range of baseline risk. …”
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563 Optimize efficiency in clinical trial development by assessing and forecasting operationally associated risks utilizing REDCap by David Harwood, Laura N. Hanson, L. Wrenn, Jessi A McManus, Taylor M. Galloway, Katelyn H. Register, Sarah J. Voitik, Morgan A. Taylor, Carol A. Griffin, Gregg S. Day

“…We anticipate a reduction in the amount of time elapsed and effort expended developing projects with scores reflecting identified project development-related risk factors. …”
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Towards a Sustainable Disruptive Growth Model: Integrating Foresight, Wild Cards and Weak Signals Analysis by Rafael Popper, Yuli Villarroel, Raimund Popper

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Fairness in focus: quantitative insights into bias within machine learning risk evaluations and established credit models by Jacob Ford

“…This research addresses how algorithmic bias may be quantified by conducting a case study of a machine learning alternative credit risk metric, comparing threshold effects for various protected classes contrasted with a conventional credit score (FICO). …”
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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence by Lesia Mochurad, Viktoriia Babii, Yuliia Boliubash, Yulianna Mochurad

“…Abstract The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models in terms of efficiency and interpretability, we propose to integrate modern machine learning algorithms and data dimensionality reduction methods, in particular XGBoost and optimized principal component analysis (PCA), which provide data structuring and increase processing speed, especially for large datasets. …”
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