Prediction of early recurrence of pancreatic ductal adenocarcinoma after resection. by Toshitaka Sugawara, Daisuke Ban, Jo Nishino, Shuichi Watanabe, Aya Maekawa, Yoshiya Ishikawa, Keiichi Akahoshi, Kosuke Ogawa, Hiroaki Ono, Atsushi Kudo, Shinji Tanaka, Minoru Tanabe

“…We employed Elastic Net, a sparse modeling method, to construct models predicting early recurrence using these multiple preoperative factors. …”
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Development of an Injury Burden Prediction Model in Professional Baseball Pitchers by Garrett Bullock, Charles Thigpen, Gary Collins, Nigel Arden, Thomas Noonan, Michael Kissenberth, Ellen Shanley

“…A model to predict arm injury burden was produced using zero inflated negative binomial regression. …”
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Factors Predicting CT Pulmonary Angiography Results in the Emergency Department by Nika Rakuša, Zrinka Sertić, Maja Prutki, Ana Marija Alduk, Ivan Gornik

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Deep Learning Models for Predicting the Recurrence of Idiopathic Granulomatous Mastitis by Li L, Yang W, Jia H

“…This study aims to evaluate and compare the performance of different machine learning models, including logistic regression, random forest, and neural networks, in predicting IGM recurrence using patient data.Methods: A retrospective analysis was conducted on 212 patients diagnosed with IGM. …”
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Machine learning models for predicting tibial intramedullary nail length by Sercan Capkin, Ali Ihsan Kilic, Hakan Cici, Mehmet Akdemir, Mert Kahraman Marasli

“…This study employs anthropometric measurements to evaluate and contrast the efficacy of machine learning (ML) models in predicting tibial IMN length. …”
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Application Value of STOP-Bang Questionnaire in Predicting Abnormal Metabolites by Pang Q, Han L, Li J, Xu L, Wang Y

“…Qingyi Pang,1 Li Han,1 Jun Li,2 Lingling Xu,1 Yueheng Wang3 1Department of Endocrinology, Peking Union Medical College Hospital, Beijing, People’s Republic of China; 2Department of Endocrinology, Beijing Hepingli Hospital, Beijing, People’s Republic of China; 3Department of Ear, Nose and Throat, Beijing Hepingli Hospital, Beijing, People’s Republic of ChinaCorrespondence: Lingling Xu; Yueheng Wang, Email llxush@163.com; 8108374@qq.comObjective: To evaluate the application value of STOP-Bang questionnaire (SBQ) in predicting abnormal metabolites.Methods: Totally 121 patients were included into the study and filled the questionnaires, and their clinical data were collected at the same time. …”
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Aortic calcium score predicts early outcomes in aortoiliac revascularization by Adalberto Batalha Megale, Nelson Wolosker, Vitoria Magliari Kalil, João Mário Nigro, Carolina Sciammarella Wakisaka, Bianca Oberhuber Dias, Marcelo Passos Teivelis, Marcelo Assis Rocha, Cynthia de Almeida Mendes

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Pupillometry in the Emergency Department: A Tool for Predicting Patient Disposition by Hector Gonzalez Jr., Yanying Chen, Newton Addo, Debbie Y. Madhok

“…Traditional methods lack precision and consistency. Our goal was to evaluate the prognostic capability of the neurological pupil index (NPI) in predicting patient disposition from within the ED. …”
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Factors Predicting Uterine Rupture Following a Cesarean Section by Shehla Tabassum, Bushra Khan, Hina Zahra Qureshi, Humaira Imran

“…Objective: This study was conducted to evaluate factors that predict uterine rupture following a previous cesarean section. …”
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Analysis and prediction of atmospheric ozone concentrations using machine learning by Stephan Räss, Stephan Räss, Markus C. Leuenberger, Markus C. Leuenberger

“…As a first step, we used techniques like best subset selection to determine the measurement parameters that might be relevant for the prediction of ozone concentrations; in general, the parameters identified by these methods agree with atmospheric ozone chemistry. …”
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Can the Oxygen Saturation Index Predict Severe Bronchopulmonary Dysplasia? by Hulya Ozdemir, Sinem Gulcan Kersin, Asli Memisoglu, Ibrahim Kandemir, Hulya Selva Bilgen

“…Early detection of severe BPD can improve treatment outcomes. This study aims to evaluate the correlation between the oxygen saturation index (OSI) and severe BPD/death in preterm infants, with a focus on the OSI’s predictive value. …”
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Inflammatory marker comparison in childhood brucellosis: predicting osteoarticular involvement by Elif Böncüoğlu, Şadiye Kübra Tüter Öz, Zafer Bağcı

“…This study aimed to compare inflammatory markers in children diagnosed with brucellosis, distinguishing between those with and without osteoarticular involvement (OI). Methods. In this retrospective study, patients diagnosed with brucellosis from 1 month to 18 years of age were evaluated. …”
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TBESO-BP: an improved regression model for predicting subclinical mastitis by Kexin Han, Yongqiang Dai, Huan Liu, Junjie Hu, Leilei Liu, Zhihui Wang, Liping Wei

“…The primary objective is to discern models by exhibiting higher predictive accuracy and lower error values.ResultsThe evaluation of the TBESO-BP model in the test phase reveals a coefficient of determination R2 = 0.94, a Mean Absolute Error (MAE) of 2.07, and a Root Mean Square Error (RMSE) of 5.33. …”
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Impact of Selection Signature on Genomic Prediction and Heritability Estimation in Livestock by Hongzhi Zhang, Zhixu Pang, Wannian Wang, Liying Qiao, Wenzhong Liu

“…We evaluated the performance of two models: (1) selection-adjusted genomic best linear unbiased prediction (GBLUP-S), and (2) MAF-stratified selection-adjusted genomic best linear unbiased prediction (GBLUP-SMS). …”
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Role of conscious awareness and Big Five in predicting the digital addiction by Yıldız Erzincanli, Fatma Geçikli

“…In this context, the present study aims to determine the levels of conscious awareness and digital addiction among university students and to examine the predictive effect of conscious awareness levels and Big Five variables on predicting digital addiction behaviors.MethodsThe present study is designed to employ the survey method. …”
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Prediction of coalbed methane productivity based on neural network models by JIN Yi, ZHENG Chenhui, SONG Huibo, MA Jiaheng, YANG Yunhang, LIU Shunxi, ZHANG Kun, NI Xiaoming

“…Finally， according to the classification results， combined with the actual drainage data， the BP and LSTM neural network algorithms were used to predict the daily gas production of CBM wells.ResultsThe results show that： （1） Based on the grey correlation method model analysis， 10 parameters such as permeability， gas saturation and reservoir pressure gradient in the study area are the key factors affecting the gas production performance of coalbed methane； （2） Using fuzzy mathematics evaluation method to evaluate the enrichment of coalbed methane， the gas production effects of 34 wells in the study area is divided into three categories： favorable area， relatively favorable area and unfavorable area. （3）A coal reservoir daily gas production prediction model was established based on the LSTM algorithm， with a prediction error value between 4.06% and 14.79%， and the average error value of 11.09%. …”
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An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction by Zechen Peng, Shaoxing Mo, Alexander Y. Sun, Jichun Wu, Xiankui Zeng, Miao Lu, Xiaoqing Shi

“…Despite recent advances in machine learning (ML) methods for GWL prediction, further improvements may be made in uncertainty quantification and model interpretability. …”
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Albumin: a novel biomarker for predicting intraoperative hypothermia in HSCR by Xiaohui Huang, Mobai Ren, Junrong Pan, Ene Huang, Yanhong Li, Donghao Guo, Junjie Wang

“…Models were adjusted for covariates, and interaction terms were evaluated for albumin (ALB) and intraoperative hypothermia. …”
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Hyperspectral Imaging for Non-Destructive Moisture Prediction in Oat Seeds by Peng Zhang, Jiangping Liu

“…To further refine the predictive model, three feature selection methods—successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA)—were assessed. …”
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Brain Morphometry and Cognitive Features in the Prediction of Irritable Bowel Syndrome by Arvid Lundervold, Ben René Bjørsvik , Julie Billing , Birgitte Berentsen , Gülen Arslan Lied , Elisabeth K. Steinsvik , Trygve Hausken , Daniela M. Pfabigan , Astri J. Lundervold 

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