Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study by Huanhuan Ren, Haojie Song, Shaoguo Cui, Hua Xiong, Bangyuan Long, Yongmei Li

“…The ensemble model predicted PH and PH-2 with AUROC of 0.858 and 0.806, respectively. …”
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Development and Validation of an MRI‐Based Radiomics Nomogram to Predict the Prognosis of De Novo Oligometastatic Prostate Cancer Patients by Wen‐Qi Liu, Yu‐Ting Xue, Xu‐Yun Huang, Bin Lin, Xiao‐Dong Li, Zhi‐Bin Ke, Dong‐Ning Chen, Jia‐Yin Chen, Yong Wei, Qing‐Shui Zheng, Xue‐Yi Xue, Ning Xu

“…ABSTRACT Objective We aimed to develop and validate a nomogram based on MRI radiomics to predict overall survival (OS) for patients with de novo oligometastatic prostate cancer (PCa). …”
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Noninvasive diagnosis model for predicting significant liver inflammation in patients with chronic hepatitis B in the immune-tolerant phase by Shanshan Chen, Lu Huang, Yili Chu, Jiangshan Lian, Hui Shao, Tingting Wang, Xuehan Zou, Haijun Huang

“…Then the least absolute shrinkage and selection operator and multivariable logistic regression were used to identify the significant independent risk factors and establish a predictive model. A diagnostic nomogram was constructed. …”
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A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks by Mohsen Shayanmehr, Sepehr Aarabi, Ahad Ghaemi, Alireza Hemmati

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Effects of Cement Treatment on Water Retention Behavior and Collapse Potential of Gypseous Soils: Experimental Investigation and Prediction Models by Hussein Q. Abdulameer AlNaddaf, Saeed Kouzegaran, Mohammed Y. Fattah, Ali Akhtarpour

“…Additionally, multiple pedotransfer functions (PTFs) were assessed to identify or adapt prediction equation(s) for the SWRC of gypseous soil both with and without cement treatment with acceptable accuracy. …”
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Predicting the reduction in heatstroke and heart disease-related mortality under urban modification scenarios using machine learning by Yukitaka Ohashi, Ko Nakajima, Yuya Takane, Yukihiro Kikegawa, Tomohiko Ihara, Kazutaka Oka

“…After meteorological changes from the control case to four urban modification scenarios were predicted using the WRF–CMBEM, potential reductions in heat-related deaths were estimated using previously successful ML-trained models. …”
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Research on the Combined Prediction Model of Residential Building Energy Consumption Based on Random Forest and BP Neural Network by Xuenan Zhang, Jinxin Zhang, Jinhua Zhang, YuChuan Zhang

“…In order to evaluate the energy consumption of residential building more effectively, this paper proposes a combined prediction model based on random forest and BP neural network (RF-BPNN). …”
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CDR-Detector: a chronic disease risk prediction model combining pre-training with deep reinforcement learning by Shaofu Lin, Shiwei Zhou, Han Jiao, Mengzhen Wang, Haokang Yan, Peng Dou, Jianhui Chen

Subjects: “…Chronic risk prediction…”
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Prediction of Hydraulic Automatic Transmission Reliability Using Failure Data Based on Exponential Decay Oscillation Distribution Model by Yongming Liu, Guowen Ye, Zhuanzhe Zhao

“…Aiming at the problem that the traditional reliability models of mechanical products are used to predict the reliability of hydraulic automatic transmission and the expected result is relatively large, firstly, the empirical distribution model line is used to statistically analyze the failure distribution law of the hydraulic automatic transmission; then, the Fourier transform is used to perform frequency domain analysis on experience distribution; on this basis, comprehensively consider the characteristics of experience distribution and frequency domain characteristics of experience distribution, constructs the reliability model of exponential decay oscillation distribution and the corresponding reliability, failure efficiency and average life calculation model; meanwhile, studies the influence of attenuation coefficient, oscillation amplitude, oscillation angle frequency, and other parameters on the probability distribution characteristics. …”
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Bladder Horn Appearance on Ultrasonographic Examination Prior to Cesarean Section: Does it Predict High Cranial Attachment of the Bladder? by İnci Kahyaoğlu, Fulya Kayıkçıoğlu, Serhan Kahyaoğlu, Sezin Ertürk Akasakal, Leyla Mollamahmutoğlu

“… OBJECTIVE: To investigate the value of bladder horn appearance seen on ultrasonography performed prior to cesarean section in prediction of high cranial attachment of bladder. STUDY DESIGN: The study was carried out in delivery unit of a teaching hospital. …”
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Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients by Xiaoqin Ge, Zhenzhen Liu, Xuehua Jiao, Xueyan Yin, Xiujie Wang, Gengxu Li

“…The score shows unprecedented survival prediction and hence constitutes a huge step towards personalized management. …”
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Role of Body Mass Index, Waist-to-Height and Waist-to-Hip Ratio in Prediction of Nonalcoholic Fatty Liver Disease by Rui-Dan Zheng, Zhuo-Ran Chen, Jian-Neng Chen, Yan-Hui Lu, Jie Chen

“…To investigate the anthropometric indicators that can effectively predict the nonalcoholic fatty liver disease (NAFLD). …”
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Prediction of Long-Term Prestress Loss and Crack Resistance Analysis of Corroded Prestressed Concrete Box-Girder Bridges by Yiming Yang, Huang Tang, Yu Mao, Xinzhong Wang

“…In this paper, a novel prediction model of long-term prestress loss considering the coupling effect of shrinkage and creep of concrete, prestressing steel relaxation, and presence of nonprestressing steel as well as the corrosion of prestressing and nonprestressing steel is proposed. …”
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Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening by Hao Li, Zhijian Liu, Kejun Liu, Zhien Zhang

“…Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. …”
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All-Cause Mortality Prediction in Subjects with Diabetes Mellitus Using a Machine Learning Model and Shapley Values by Oana Mirea, Mostafa Ghelich Oghli, Oana Neagoe, Mihaela Berceanu, Eugen Țieranu, Liviu Moraru, Victor Raicea, Ionuț Donoiu

“…We aimed to develop and test an ML model to predict all-cause mortality in a large cohort of subjects with DM. …”
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Normal Hearing Ability but Impaired Auditory Selective Attention Associated with Prediction of Response to Donepezil in Patients with Alzheimer’s Disease by Yoshitaka Ouchi, Kenichi Meguro, Kyoko Akanuma, Yuriko Kato, Satoshi Yamaguchi

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Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study by Fisnik Dalipi, Sule Yildirim Yayilgan, Alemayehu Gebremedhin

“…We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). …”
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Retracted: Development and Validation of a Risk Prediction Model for Ventricular Arrhythmia in Elderly Patients with Coronary Heart Disease by Cardiology Research and Practice

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A New Method for Predicting Residual Strength of Rock in Water Diversion Tunnel Using Drilling Process Monitoring by Wanfei Gao

“…A new field method using drilling process monitoring was proposed to predict the peak strength and residual strength. The results show that the predicted strength for four types of rock is in good agreement with those of the standard tests in laboratory. …”
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Construction of a prognostic prediction model for colorectal cancer based on 5-year clinical follow-up data by Boao Xiao, Min Yang, Yao Meng, Weimin Wang, Yuan Chen, Chenglong Yu, Longlong Bai, Lishun Xiao, Yansu Chen

“…The model performance was comprehensively assessed using multiple metrics, including accuracy, F1 score, Brier score, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic curve, calibration curve, and decision curve analysis curve. …”
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