Predictive value of epicardial fat thickness for left ventricular reverse remodeling after radiofrequency ablation in atrial fibrillation patients with left ventricular hypertrophy by Ke Hu, Wenhui Wang, Junping Zhen, Chunsong Kang

“…[Objective] To investigate the predictive value of epicardial fat thickness (EFT) for left ventricular reverse remodeling after radiofrequency ablation in patients with left ventricular hypertrophy. …”
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Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer by Nicolas Captier, Marvin Lerousseau, Fanny Orlhac, Narinée Hovhannisyan-Baghdasarian, Marie Luporsi, Erwin Woff, Sarah Lagha, Paulette Salamoun Feghali, Christine Lonjou, Clément Beaulaton, Andrei Zinovyev, Hélène Salmon, Thomas Walter, Irène Buvat, Nicolas Girard, Emmanuel Barillot

“…In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. …”
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Corrigendum: Predicted indirectly recognizable HLA epitopes scores and clinical outcomes after haploidentical stem cell transplantation in pediatric patients with relapsed neuroblastoma by Eun Seop Seo, Eun Seop Seo, In Hwa Jeong, In Hwa Jeong, Hee Young Ju, Ju Kyung Hyun, Ji Won Lee, Keon Hee Yoo, Won Young Heo, Ki Woong Sung, Hee Won Cho, Eun-Suk Kang

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Role of Age, Comorbidity, and Frailty in the Prediction of Postoperative Complications After Surgery for Vulvar Cancer: A Retrospective Cohort Study with the Development of a Nomogram by Giovanni Delli Carpini, Francesco Sopracordevole, Camilla Cicoli, Marco Bernardi, Lucia Giuliani, Mariasole Fichera, Nicolò Clemente, Anna Del Fabro, Jacopo Di Giuseppe, Luca Giannella, Enrico Busato, Andrea Ciavattini

“…The objective of this study was to evaluate the role of age, comorbidities, and frailty in predicting postoperative complications after vulvar cancer surgery and to develop a predictive nomogram. …”
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Does structural compromise of the aorta in patients with aortic pathologies predict increased spinal and vascular complications and reoperations in patients undergoing anterior approach to the spine? by Neil V. Shah, Pelin Çeliker, Cirous Sadeghi, Rachel Baum, Juhayer S. Alam, Chibuokem Ikwuazom, Peter L. Zhou, Benjamin Krasnyanskiy, David Kim, Rohan Desai, Sandeep Bains, Jeffrey M. Schwartz, Bassel G. Diebo, Carl B. Paulino

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The Role of Mature Brain-Derived Neurotrophic Factor and Its Precursor in Predicting Early-Onset Insomnia in Stroke Patients Experiencing Early Neurological Deterioration by Shi G, Yu P, Wang Z, Xu M, Guo M, Wang X, Zhou R

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Dynamic Prediction Research of Silicon Content in Hot Metal Driven by Big Data in Blast Furnace Smelting Process under Hadoop Cloud Platform by Yang Han, Jie Li, Xiao-Lei Yang, Wei-Xing Liu, Yu-Zhu Zhang

“…The results of the research show that the structural and parallel SVM algorithms in the hot metal [Si] content value dynamic prediction hit rate and lifting dynamic prediction hit rate were 91.2% and 92.2%, respectively. …”
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Advanced Efficient Feature Selection Integrating Augmented Extreme Learning Machine and Particle Swarm Optimization for Predicting Nitrogen Use Efficiency and Yield in Corn by Josselin Bontemps, Isa Ebtehaj, Gabriel Deslauriers, Alain N. Rousseau, Hossein Bonakdari, Jacynthe Dessureault-Rompré

“…Machine learning techniques, such as augmented extreme learning machine (AELM) and particle swarm optimization (PSO), were employed to optimize nitrogen recommendations by identifying the most relevant features for predicting yield and nitrogen use efficiency (NUE). …”
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Preferences of pregnant women and obstetric healthcare professionals on using noninvasive prenatal testing to predict adverse pregnancy outcomes: a discrete choice experiment by Mireille N Bekker, Ewoud Schuit, Ian Smith, Ellis Becking, Elisabeth Akkerman, Peter Scheffer, Daphne Stemkens, Caroline J Bax, Neeltje Crombag, Lidewij Henneman

“…Overall, 74% (n=1181) of pregnant women (strongly) agreed that NIPT to predict adverse pregnancy outcomes should be offered to pregnant women compared with 49% (n=151) of obstetric healthcare professionals.Conclusions Most pregnant women have a positive attitude towards using NIPT to predict adverse pregnancy outcomes compared with slightly less than half of the obstetric healthcare professionals. …”
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Determinants of fertility in timed artificial insemination programs in beef cattle: predictive ability and risk factors from almost 2 million data points by R.E.F. Assis, F. Baldi, L.B. Temp, R. Ungerfeld, M.F. de Sá Filho

“…Farm, technician, and sire were the variables with the highest predictive ability for P/AI. At the same time, breeding season, climatic region, and time of female availability were the variables with the lowest predictive ability. …”
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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

“…The prediction and profiler plots are also confirm the validity of the ANNs model performance. …”
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Predicting cytomegalovirus infection and graft-versus-host disease using QuantiFERON-CMV and Monitor in pediatric transplants: a proof-of-concept study by Lina Souan, Rawad Rihani, Maher A. Sughayer

“…Methods: The QF-CMV and QFM tests were used to predict CMV pp65 antigen and GvHD in 24 pediatric hematopoietic stem cell transplant recipients. …”
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Waist-to-Height Ratio Is a Better Anthropometric Index than Waist Circumference and BMI in Predicting Metabolic Syndrome among Obese Mexican Adolescents by Edel Rafael Rodea-Montero, María Lola Evia-Viscarra, Evelia Apolinar-Jiménez

“…The overall criterion (WHtR ≥ 0.6) could be appropriate for predicting MS in obese Mexican adolescents.…”
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A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model by Qichun Bing, Fuxin Shen, Xiufeng Chen, Weijian Zhang, Yanran Hu, Dayi Qu

“…Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. …”
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Clinical and Renal Biopsy Findings Predicting Outcome in Renal Thrombotic Microangiopathy: A Large Cohort Study from a Single Institute in China by Xiao-Juan YU, Feng YU, Di Song, Su-Xia Wang, Yan Song, Gang Liu, Ming-Hui Zhao

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Assessing the application of adapted theory of planned behaviour in predicting postpartum family planning intentions in a pragmatic randomized control trial in Western Kenya. by Morris Senghor Shisanya, Mary Kipmerewo, Everlyne Morema, Collins Ouma

“…Structural equation modeling (SEM) was used to predict the intention for early PPFP in the adapted TPB.…”
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The predicted value for venous thromboembolism based on a modified model of caprini combined with fasting blood glucose and D-dimer: a retrospective study by Ping Wang, Yusi Yang, Lin Zheng, Xiaotong Qi, Sheng Yan, Honglin Dong

Subjects: “…Prediction model…”
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Computational Studies on the Molecule 1-(2-Hydroxyethyl)-5-Fluorouracil in Gas Phase and Aqueous Solution and Prediction of Its Confinement inside Capped Nanotubes by Y. Tadjouteu Assatse, G. W. Ejuh, F. Tchoffo, J. M. B. Ndjaka

“…The analysis of thermodynamic properties leads us to predict that the confinement of the studied molecule inside capped nanotubes SWCNT(12,0), SWCNT(14,0), and SWCNT(16,0) is possible. …”
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Dysregulation of c-Jun (JUN) and FBJ murine osteosarcoma viral oncogene homolog B (FOSB) in obese people and their predictive values for metabolic syndrome by Chenxi Yang, Yi Chen, Guangfeng Tang, Tongtong Shen, Li Li

“…This study aimed to identify new biomarkers for predicting MetS and provide a novel diagnostic approach. …”
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Perceptions of healthcare professionals on the use of a risk prediction model to inform atrial fibrillation screening: qualitative interview study in English primary care by Chris Gale, Jianhua Wu, Ramesh Nadarajah, Catherine Reynolds, Ellen Hamilton, Lydia Shone

“…The perceptions of healthcare professionals (HCPs) towards the implementation of risk prediction models to target AF screening are unknown. …”
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