The prealbumin-CD19+ index predicts surgical survival in patients with GC by Hongming Pan, Hao Sun, Yanjiao Zuo, Ruihu Zhao, Yingwei Xue, Hongjiang Song

“…Abstract Objective This study aimed to establish a prealbumin (PALB)-CD19+ index that combines nutritional and immune statuses to comprehensively evaluate the prognosis of GC patients undergoing surgery. …”
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MMTraP: Multi-Sensor Multi-Agent Trajectory Prediction in BEV by Sushil Sharma, Arindam Das, Ganesh Sistu, Mark Halton, Ciaran Eising

“…The proposed approach has been rigorously evaluated using the nuScenes dataset. Results show that our method improves the accuracy of trajectory predictions and outperforms state-of-the-art techniques, particularly in challenging environments such as congested urban areas. …”
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Development and Validation of an Incident Hypertension Risk Prediction Model for Young Adults by Jaejin An, Heidi Fischer, Liang Ni, Mengying Xia, Soon Kyu Choi, Kerresa L. Morrissette, Rong Wei, Kristi Reynolds, Paul Muntner, Lisandro D. Colantonio, Andrew E. Moran, Brandon K. Bellows, Monika M. Safford, Norrina B. Allen, Vanessa Xanthakis, Carmen R. Isasi, Linda C. Gallo, Krista M. Perreira, Yiyi Zhang

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Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients by Mengxin Lu, Shuai Tao, Xinyan Li, Qunling Yang, Cong Du, Weijia Lin, Shuangshuang Sun, Conglin Zhao, Neng Wang, Qiankun Hu, Yuxian Huang, Qiang Li, Yi Zhang, Liang Chen

“…Multivariate logistic regression analysis showed that LTBP2, PLT and AST levels were demonstrated as the independent prediction factors. A nomogram that included the three factors was tabled to evaluate the probability of significant fibrosis occurrence. …”
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Comparison of Intensive Care Scoring Systems in Predicting Overall Mortality of Sepsis by Mustafa Ozgur Cirik, Guler Eraslan Doganay, Melek Doganci, Tarkan Ozdemir, Murat Yildiz, Abdullah Kahraman, Seray Hazer, Mehtap Tunc, Kerem Ensarioglu, Azra Ozanbarci, Oral Mentes

“…This study aims to compare the scores (APACHE II, SOFA, SAPS II, OASIS) in terms of their role in predicting overall mortality in patients admitted to ICUs with a diagnosis of sepsis or septic shock. …”
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Perinatal artificial intelligence in ultrasound (PAIR) study: predicting delivery timing by Neil Patel, John O’Brien, Robert Bunn, Brandon Schanbacher, John Bauer, Garrett K. Lam

“…OBJECTIVE To evaluate the ability of a proprietary artificial intelligence (AI) model to predict the number of days until delivery using ultrasound images alone and to assess the continuous improvement of prediction accuracy, particularly for preterm births, through model retraining.METHODS An AI software was developed and trained using de-identified ultrasound images from a cohort of women who delivered at the University of Kentucky from 2017 to 2021. …”
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Construction and validation of a presenteeism prediction model for ICU nurses in China by Jijun Wu, Yuxin Li, Yuxin Li, Xiaoli Liu, Yuting Fan, Ping Dai, Baixia Chen, Zhenfan Liu, Xian Rong, Xiaoli Zhong

“…Univariate and multifactorial logistic regression analyses were used to determine the influencing factors for presenteeism, and R software was used to construct a column-line graph prediction model. The differentiation and calibration of the predictive model were evaluated by the area under the curve of subjects’ work characteristics (ROC) and the Hosmer-Leme-show test, and the clinical decision curve evaluated the clinical validity of the predictive model.ResultsThe presenteeism rate of ICU nurses in the development set was 76.8%. …”
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Comparative analysis of four nutritional scores in predicting delirium in ICU patients by Chunchun Yu, Lefu Chen, Xiong Lei, Zhixiao Xu, Hongjun Zhao, Chengshui Chen, Chengshui Chen

“…BackgroundThe nutritional assessment indicators for critically ill patients are diverse, with limited research about comparing the predicting value of different nutritional assessment tools for delirium in the intensive care unit (ICU).ObjectivesThe study aimed to validate the relationship between malnutrition and ICU delirium and explore the optimal nutritional scores for predicting ICU delirium.MethodsThis study was based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and included 319 ICU patients who met the inclusion and exclusion criteria. …”
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Predictability of ClinCheck in Overbite Correction with Aligners: A Systematic Review by Michela Boccuzzi, Saverio Cosola, Andrea Butera, Annamaria Genovesi, Teresa Laborante, Attilio Castaldo, Agostino Zizza, Giacomo Oldoini, Alessandro Nota, Simona Tecco

“…Methods: The research question focused on the effectiveness of ClinCheck in predicting the actual correction of deep bite AND open bite in adult patients. …”
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Prediction and analysis of toxic and side effects of tigecycline based on deep learning by Yin Xiong, Guoxin Liu, Xin Tang, Boyang Xia, Yalian Yu, Guangjun Fan

“…The safety of patients still needs further study.MethodsIn this study, the clinical data of 263 patients with pulmonary infection in Shengjing Hospital of China Medical University and the Second Affiliated Hospital of Dalian Medical University were collected retrospectively, and the hepatotoxicity prediction model was established. …”
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Predicting hypoglycemia in elderly inpatients with type 2 diabetes: the ADOCHBIU model by Rui-Ting Zhang, Yu Liu, Chao Sun, Quan-Ying Wu, Hong Guo, Gong-Ming Wang, Ke-Ke Lin, Jing Wang, Xiao-Yan Bai

“…The purpose of the study is to construct a nomogram prediction model for the risk of hypoglycemia in elderly inpatients with T2DM and to evaluate the predictive performance of the model.MethodsFrom August 2022 to April 2023, 546 elderly inpatients with T2DM were recruited in seven tertiary-level general hospitals in Beijing and Inner Mongolia province, China. …”
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Dynamic Nomogram for Predicting the Fall Risk of Stroke Patients: An Observational Study by Wu Y, Jiang X, Wang D, Xu L, Sun H, Xie B, Tan S, Chai Y, Wang T

“…Yao Wu,1,2,* Xinjun Jiang,1,* Danxin Wang,3 Ling Xu,1 Hai Sun,1 Bijiao Xie,1 Shaoying Tan,3 Yong Chai,4 Tao Wang1,5 1International Nursing School, Hainan Medical University, Haikou, Hainan, People’s Republic of China; 2School of Nursing, Leshan Vocational and Technical College, Leshan, SiChuan, People’s Republic of China; 3Department of Nursing, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan, People’s Republic of China; 4Nursing Department of the Second People’s Hospital of Yibin, Yibin, Sichuan, People’s Republic of China; 5Foshan University, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tao Wang, International Nursing School, Hainan Medical University, Xueyuan Road, Longhua District, Haikou, Hainan, People’s Republic of China, Email lilywang7499@gmail.comBackground: Common fall risk assessment scales are not ideal for the prediction of falls in stroke patients. The study aimed to develop and verify a dynamic nomogram model for predicting the falls risk in stroke patients during rehabilitation.Methods: An observational study design was adopted, 488 stroke patients were treated in a tertiary hospital from March to September 2022 were investigated for fall risk factors and related functional tests. …”
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Signature of immune-related metabolic genes predicts the prognosis of hepatocellular carcinoma by Weibin Zhuo, Hongmei Xia, Bin Lan, Bin Lan, Bin Lan, Yu Chen, Yu Chen, Yu Chen, Xuefeng Wang, Xuefeng Wang, Xuefeng Wang, Jingfeng Liu, Jingfeng Liu, Jingfeng Liu

“…However, there is still a lack of comprehensive understanding regarding the immune-related metabolic genes that can accurately reflect the prognosis of HCC.MethodsIn order to address this issue, we developed a prognostic prediction model based on immune and metabolic genes. …”
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Metabolomics identify serum biomarkers for predicting acute exacerbation and severity of bronchiectasis by Jiaxin Yan, Fanxin Deng, Xueli Wang, Jing Wei, Yang Cao, Kaili Deng, Xiaolin Chen, Lei Shu, Lei Shi, Mingjing Wu, Ganzhu Feng

“…This study aimed to identify novel diagnostic metabolic biomarkers for predicting acute exacerbation and severity of BE. Methods: A liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomic analysis was performed for serum samples from 45 patients with acute BE exacerbation and 15 healthy controls. …”
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Plasma cytomegalovirus DNA load predicts outcomes in liver transplant recipients by Hao‐Chien Hung, Po‐Jung Hsu, Jin‐Chiao Lee, Yu‐Chao Wang, Chih‐Hsien Cheng, Tsung‐Han Wu, Ting‐Jung Wu, Hong‐Shiue Chou, Kun‐Ming Chan, Wei‐Chen Lee, Chen‐Fang Lee

“…Abstract Objective Cytomegalovirus (CMV) infection has a significant negative impact on liver transplant (LT) recipients. We aimed to evaluate the efficacy of real‐time DNA quantitative polymerase chain reaction (qPCR) in the early detection of CMV and predicting post‐transplant outcomes. …”
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Analysis of epidemiology and nomogram construction for prediction and clinical decision-making in gliomas by Yuxin Zhao, Zihan Xu, Zihan Xu, Zihan Xu, Ying Liu, Ming Ye, Rui Chen, Zhongyu Cao, Hong Zhou, Yang Zhou

“…Exploring the epidemiologic characteristics and prognostic factors of gliomas, and constructs a nomogram-based predictive model can help to evaluate the public health impact, optimize risk stratification, and guide treatment decision-making.MethodsThis cross-sectional epidemiological analysis used the most recently released data from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2000, to December 31, 2019. …”
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Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome by Oshrit Shtossel, Adi Eshel, Shalev Fried, Mika Geva, Ivetta Danylesko, Ronit Yerushalmi, Noga Shem-Tov, Joshua A. Fein, Marco Fabbrini, Avichai Shimoni, Sondra Turjeman, Yoram Louzoun, Arnon Nagler, Omry Koren, Roni Shouval

“…Methods We applied the RATIO (suRvival Analysis lefT barrIer lOss) model to longitudinal stool and saliva microbiome data from 204 adult HSCT recipients to predict the timing of seven outcomes: overall survival (OS), non-relapse mortality (NRM), relapse, acute GVHD (grades II–IV and III–IV), chronic GVHD, and oral chronic GVHD. …”
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A novel model for predicting immunotherapy response and prognosis in NSCLC patients by Ting Zang, Xiaorong Luo, Yangyu Mo, Jietao Lin, Weiguo Lu, Zhiling Li, Yingchun Zhou, Shulin Chen

“…Methods Patients were randomly divided into training cohort and validation cohort at a ratio of 2:1. …”
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Improving cardiovascular risk stratification: the role of abdominal obesity in predicting MACEs by Carlo De Matteis, Stefano Petruzzelli, Giusi Graziano, Fabio Novielli, Ersilia Di Buduo, Salvatore Cantatore, Elsa Berardi, Gianfranco Antonica, Maria Arconzo, Marica Cariello, Marilina Florio, Lucilla Crudele, Antonio Moschetta

“…This study aimed to evaluate the predictive utility of baseline cardiometabolic risk factors, with a particular focus on abdominal obesity as quantified by waist circumference (WC), alongside established 10-year CVR scores, for incident Major Adverse Cardiovascular Events (MACEs). …”
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Revolutionizing heart attack prognosis: Introducing an innovative regression model for prediction by Hanaa Albanna, Madhav Raj Theeng Tamang, Chandan Patel, Mhd Saeed Sharif

“…It is expected that this cross-disciplinary approach will underline the role of machine learning in the mitigation of the heart disease burden and optimization of resources spent on healthcare. Methods:: This study explores the application of machine learning techniques for predicting heart attack risk using structured clinical data. …”
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