Comprehensive Second-Order Adjoint Sensitivity Analysis Methodology (2nd-ASAM) Applied to a Subcritical Experimental Reactor Physics Benchmark: I. Effects of Imprecisely Known Micr... by Dan G. Cacuci, Ruixian Fang, Jeffrey A. Favorite

“…The numerical results for <i>all</i> of these sensitivities, together with discussions of their major impacts, will be presented in a sequence of publications in the Special Issue of <i>Energies</i> dedicated to <i>&#8220;Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems</i><i>&#8221;.…”
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Relationship between metastasis and second primary cancers in women with breast cancer by Chaofan Li, Mengjie Liu, Jia Li, Xixi Zhao, Yusheng Wang, Xi Chen, Weiwei Wang, Shiyu Sun, Cong Feng, Yifan Cai, Fei Wu, Chong Du, Yinbin Zhang, Shuqun Zhang, Jingkun Qu

“…XGBoost, a high-precision machine learning algorithm, was used to create a prediction model to estimate the prognosis of metastatic breast cancer (MBC) patients with SPCs.ResultsThe results reveal that the SIR (1.01; 95% CI, 0.99–1.03, p&gt;0.05) of SPCs in non-metastasis breast cancer (NMBC) patients was similar to the general population. …”
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Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling by Stefanie Nickels, Matthew D Edwards, Sarah F Poole, Dale Winter, Jessica Gronsbell, Bella Rozenkrants, David P Miller, Mathias Fleck, Alan McLean, Bret Peterson, Yuanwei Chen, Alan Hwang, David Rust-Smith, Arthur Brant, Andrew Campbell, Chen Chen, Collin Walter, Patricia A Arean, Honor Hsin, Lance J Myers, William J Marks Jr, Jessica L Mega, Danielle A Schlosser, Andrew J Conrad, Robert M Califf, Menachem Fromer

“…Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.…”
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Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Occult Breast Cancer Patients by Jingkun Qu, Chaofan Li, Mengjie Liu, Yusheng Wang, Zeyao Feng, Jia Li, Weiwei Wang, Fei Wu, Shuqun Zhang, Xixi Zhao

“…The effectiveness of clinical application of the predictive models was validated using decision curve analysis (DCA). …”
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Genotipos de Helicobacter pylori asociados con cáncer gástrico y displasia en pacientes de Colombia by Y.H. Carlosama-Rosero, C.P. Acosta-Astaiza, C.H. Sierra-Torres, H.J. Bolaños-Bravo

“…That information could be used to create a risk index in a predictive model to optimize the healthcare of higher-risk patients.…”
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Risk factors for intensive care admission in patients with COVID-19 pneumonia: A retrospective study by Abdullah Mobeireek, Saud AlSaleh, Loui Ezzat, Osama Al-saghier, Sultan Al-Amro, Abdulla Al-Jebreen, Armen Torchyan, Mohammed AlHajji, Liju Ahmed

“…However, risk factors and predictive models for disease progression in patients with COVID-19 are not consistent across the globe. …”
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Screening for lung cancer with computed tomography: protocol for systematic reviews for the Canadian Task Force on Preventive Health Care by Jennifer Pillay, Sholeh Rahman, Scott Klarenbach, Donna L. Reynolds, Laure A. Tessier, Guylène Thériault, Nav Persaud, Christian Finley, Natasha Leighl, Matthew D. F. McInnes, Chantelle Garritty, Gregory Traversy, Maria Tan, Lisa Hartling

“…Methods We will update the review on the benefits and harms of screening with CT conducted for the task force in 2015 and perform de novo reviews on the comparative effects between (i) trial-based selection criteria and use of risk prediction models and (ii) trial-based nodule classification and different nodule classification systems and on patients’ values and preferences. …”
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Novel models by machine learning to predict prognosis of breast cancer brain metastases by Chaofan Li, Mengjie Liu, Yinbin Zhang, Yusheng Wang, Jia Li, Shiyu Sun, Xuanyu Liu, Huizi Wu, Cong Feng, Peizhuo Yao, Yiwei Jia, Yu Zhang, Xinyu Wei, Fei Wu, Chong Du, Xixi Zhao, Shuqun Zhang, Jingkun Qu

“…However, there is a lack of accurate prediction models in the clinic. What’s more, primary surgery for BCBM patients is still controversial. …”
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Integrating TimeSync Disturbance Detection and Repeat Forest Inventory to Predict Carbon Flux by Andrew N. Gray, Warren B. Cohen, Zhiqiang Yang, Eric Pfaff

“…The long time series of Landsat imagery provides spatially comprehensive, consistent information that can be used to fill the gaps in ground measurements with predictive models. To evaluate such models, we relate Landsat spectral changes and disturbance interpretations directly to C flux measured on NFI plots and compare the performance of models with and without ground-measured predictor variables. …”
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Study on the Value of Oligosaccharide Chain and Alpha-fetoprotein for Risk Screening and Diagnosis of Hepatitis B Virus-related Hepatocellular Carcinoma by ZHANG Yun, CAI Xinyi, DING Jingnuo, LU Shengwei, CHEN Cuiying, WU Tingting, ZHANG Junli, ZHAO Weifeng

“…Patient data (age, gender, cirrhosis status), laboratory indices[total bilirubin (TB), albumin (ALB), platelet count (PLT), AFP]were collected through electronic medical records, and a liver cancer risk prediction model score for chronic liver disease patients (aMAP score) was calculated, along with oligosaccharide marker test results (G-Test). …”
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Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model by Shaozhe Huang, Lei Yu, Wenbing Luo, Hongzhong Pan, Yalong Li, Zhike Zou, Wenjuan Wang, Jialong Chen

“…To overcome the difficulty that existing hydrological models cannot accurately simulate hydrological processes with limited information in irrigated paddy areas in southern China, this paper presents a prediction model combining the Ensemble Empirical Mode Decomposition (EEMD) method and the Long Short-Term Memory (LSTM) network. …”
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Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage by Jianxiang Tang, Xiaoyu Wang, Hongli Wan, Chunying Lin, Zilun Shao, Yang Chang, Hexuan Wang, Yi Wu, Tao Zhang, Yu Du

“…For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny problems. …”
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GC和LF-NMR结合化学计量学方法 检测掺假油茶籽油Detection of adulterated oil-tea camellia seed oil by GC and LF-NMR combined with chemometrics methods by 胡伯凯1,2，王纪辉1,2，刘亚娜1,2，耿阳阳1,2，王港2，张东亚3 HU Bokai1,2, WANG Jihui1,2, LIU Yana1,2, GENG Yangyang1,2, WANG Gang2, ZHANG Dongya3

“…The PLS quantitative prediction models established for the adulteration of rapeseed oil, peanut oil, palm oil and high oleic acid peanut oil in oil-tea camellia seed oil showed R2 of 0.994 1, 0.998 6, 0.997 6 and 0.978 1 for the true and predicted values, respectively. …”
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Soluble interleukin-2 receptor combined with interleukin-8 is a powerful predictor of future adverse cardiovascular events in patients with acute myocardial infarction by Kunming Pan, Chenqi Xu, Chenqi Xu, Chenqi Xu, Chenqi Xu, Can Chen, Shuqing Chen, Yuqian Zhang, Xiaoqiang Ding, Xiaoqiang Ding, Xiaoqiang Ding, Xiaoqiang Ding, Xialian Xu, Xialian Xu, Xialian Xu, Xialian Xu, Qianzhou Lv

“…The addition of sIL-2R combined with IL-8 to the existing prediction model resulted in a significant improvement in predictive power (p = 0.029), prompting a 20.8% increase in the proportion of correct classifications.ConclusionsHigh serum sIL-2R combined with IL-8 levels was significantly associated with MACEs during follow-up in patients with MI, suggesting that sIL-2R combined with IL-8 may be a helpful biomarker for identifying the increased risk of new cardiovascular events. …”
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Quantifying exchangeable base cations in permafrost: a reserve of nutrients about to thaw by E. Mauclet, M. Villani, A. Monhonval, C. Hirst, E. A. G. Schuur, S. Opfergelt

“…This estimate is needed for future ecosystem prediction models to provide constraints on the size of the reservoir in exchangeable nutrients (Ca, K, Mg, and Na) about to thaw. …”
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Relationship between Prognostic Nutritional Index and Major In-hospital Adverse Cardiovascular Events after Percutaneous Coronary Intervention in Patients with Acute ST-elevation M... by ZHAO Banghao, YUAN Teng, ZHAO Ling, AMANGULI Ruze, NILUPAER Xiefukaiti, MA Yitong, YANG Yining, GAO Xiaoming

“…The AUC of PNI for predicting in-hospital MACE was 0.734 (95%CI=0.694-0.773). A predictive model was constructed by Logistic regression analysis, and the model predicted an AUC of 0.791 (95%CI=0.753-0.858) for the occurrence of in-hospital MACE after PCI in patients with STEMI complicated by T2DM. …”
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Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection by Meng-qi Jiang, Li-qiang Qian, Yu-jia Shen, Yuan-yuan Fu, Wen Feng, Zheng-ping Ding, Yu-chen Han, Xiao-long Fu

“…In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. …”
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Risk Factors of Portal Vein Thrombosis in Patients with Different Child-Pugh Classes Liver Cirrhosis by M. Yu. Nadinskaia, Kh. B. Kodzoeva, K. A. Gulyaeva, M.-D. E. Khen, D. I. Koroleva, M. A. Privalov, A. Kh. Tekaeva, V. R. Fedorov, S. G. Prokofev

“…Aim: to evaluate the frequency of portal vein thrombosis (PVT) and build predictive models of the development of PVT for patients with liver cirrhosis (LC) of A and B/C classes by Child-Pugh.Materials and methods. …”
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Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning... by Giorgio Russo, Alessandro Stefano, Pierpaolo Alongi, Albert Comelli, Barbara Catalfamo, Cristina Mantarro, Costanza Longo, Roberto Altieri, Francesco Certo, Sebastiano Cosentino, Maria Gabriella Sabini, Selene Richiusa, Giuseppe Maria Vincenzo Barbagallo, Massimo Ippolito

“…The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. …”
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Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study by Marie‐Theres Huemer, Alina Bauer, Agnese Petrera, Markus Scholz, Stefanie M. Hauck, Michael Drey, Annette Peters, Barbara Thorand

“…We implemented boosting with stability selection to enable false positives‐controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100× bootstrapping by cross‐validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors. …”
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