Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction by Jiahui Lai, Cailian Cheng, Tiantian Liang, Leile Tang, Xinhua Guo, Xun Liu

“…The extreme gradient boosting (XGBoost) algorithm exhibited superior performance across training (AUC 0.963, 95% CI: 0.951–0.975), internal validation (AUC 0.940, 95% CI: 0.924–0.956), and external validation (AUC 0.850, 95% CI: 0.832–0.868) datasets. …”
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Comparing prediction accuracy for 30-day readmission following primary total knee arthroplasty: the ACS-NSQIP risk calculator versus a novel artificial neural network model by Anirudh Buddhiraju, Michelle Riyo Shimizu, Tony Lin-Wei Chen, Henry Hojoon Seo, Blake M. Bacevich, Pengwei Xiao, Young-Min Kwon

“…This study aims to compare the predictive accuracy of the SRC with a novel artificial neural network (ANN) algorithm for 30-day readmission after primary TKA, using the same set of clinical variables from a large national database. …”
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Diagnostic value of N-terminal pro-B-type natriuretic peptide in hemodialysis patients by D. S. Sedov, E. A. Fedotov, A. P. Rebrov

“…A proportional increase in the concentration of prohormone to the increase in systolic dysfunction was found while analyzing the median NT-proBNP, both among all patients and after separation into groups depending on the hydration status. …”
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Predictive density profile control with discrete pellets, applied to integrated simulations of ITER by C.A. Orrico, M. van Berkel, T.O.S.J. Bosman, L. Ceelen, W.P.M.H. Heemels, F. Koechl, D. Krishnamoorthy

“…As a solution, we propose a predictive density profile controller that considers fuel pellets as discrete actuators, while ensuring operation within prescribed density limits. …”
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METHOD OF MOLAR SURFACE RESTORATION FOR TREATMENT OF PIT-AND-FISSURE CARIES IN CHILDREN by J.I. Soloshenko

“…Considering this progress, the features in children caries may be unnoticed for definite period and be revelaed only during preventive checkups, when the defect is identified in the fissure or pit while the occlusal surface remains intact. Occlusal surface restoration after preparation of the carious cavity requires the knowledge of anatomical features of molars, certain skills, experience in modeling cusps, slopes, fissures and pits and, of course, doctor’ attention. …”
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INFLAMMATION's cognitive impact revealed by a novel "Line of Identity" approach. by Donald R Royall, Raymond F Palmer

“…Few individuals are demented by any single biomarker, while several may independently explain small fractions of dementia severity. …”
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Calibration and Validation of NOAA-21 Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper Sensor Data Record Data by Banghua Yan, Trevor Beck, Junye Chen, Steven Buckner, Xin Jin, Ding Liang, Sirish Uprety, Jingfeng Huang, Lawrence E. Flynn, Likun Wang, Quanhua Liu, Warren D. Porter

“…The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. …”
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Characteristics of gut and lung microbiota in patients with lung masses and their relationship with clinical features by Yanping Yang, Jiacheng Shen, Sulan Wei, Maosong Ye, Xing Zhao, Jian Zhou, Lin Tong, Jie Hu, Yuanlin Song, Shengdi Wu, Nuo Xu

“…We also found Bacteroides (P = 0.01458) was abundant in NSCLC than those of SCLC in feces group, while the BALF group was dominated by norank_c_Cyanobacteria (P = 0.03384). …”
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Indoor Air Wellness: A Predictive Model for Pollution Control Using Advanced AI Techniques by Kalyan Chatterjee, Muntha Raju, Bhoomeshwar Bala, U. Nikitha, Gampala Prabhas, Naim Ahmad, Ayman Qahmash, Wade Ghribi, Bernardo Lemos, Saurav Mallik

“…The model excels in dynamic environments by refining the accuracy of the proposed Kal-ANN algorithm achieving a predictive accuracy of up to 96. 55%, a root mean square error, a Mean Absolute Error, and a Mean Squared Prediction Error as low as 9.85, 6.12, and <inline-formula> <tex-math notation="LaTeX">$3.15~g/m$ </tex-math></inline-formula>. …”
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Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images by Min Li, Xiaohan Nie, Yilidan Reheman, Pan Huang, Shuailei Zhang, Yushuai Yuan, Chen Chen, Ziwei Yan, Cheng Chen, Xiaoyi Lv, Wei Han

“…The least absolute shrinkage and selection operator (LASSO) algorithm was chosen for feature selection. In contrast to no feature selection, the model optimization time decreased by 19.94 seconds while maintaining precision. …”
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Automated ejection fraction and risk stratification in cardiomyopathy patients with diverse LV geometry using 2D echocardiography by Ziwei Zhu, Ke Fan, Shuyuan Zhang, Tingting Hu, Jingyi Li, Ze Zhao, Ye Jin, Shuyang Zhang

“…We developed a deep learning (DL) model to estimate left ventricular ejection fraction (LVEF) from echocardiographic images while accounting for LVG variability and assessed prognostic factors across LVG subtypes. …”
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Adaptive User Pairing With Non-Orthogonal Medium Access Choices for Balanced Coexistence of Mission-Critical and eMBB Services in Cellular IoT by Farnaz Khodakhah, Aamir Mahmood, Patrik Osterberg, Mikael Gidlund

“…Our objective is to enhance eMBB rates while ensuring quality of service (QoS) for MC users, assessed through average age of information (AoI) and peak AoI (PAoI) violation probabilities. …”
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Association between age and lung cancer risk: evidence from lung lobar radiomics by Yuwei Li, Chengting Lin, Lei Cui, Chao Huang, Liting Shi, Shiyang Huang, Yue Yu, Xianglan Zhou, Qian Zhou, Kun Chen, Lei Shi

“…The minimum redundancy maximum relevance algorithm was applied to identify the top 10 age-related radiomic features among 13,137 never smokers. …”
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Identification of Anoikis-Related Genes in Chronic Kidney Disease Based on Bioinformatics Analysis Combined with Experimental Validation by Liu H, Mei M, Zhong H, Lin S, Luo J, Huang S, Zhou J

“…Furthermore, we constructed a competitive endogenous RNA (ceRNA) network for the hub genes utilizing the ENCORI and miRDB databases, while also calculating Spearman correlation coefficients. …”
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A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data by Jordi Mahardika Puntu, Ping-Yu Chang, Haiyina Hasbia Amania, Ding-Jiun Lin, M. Syahdan Akbar Suryantara, Jui-Pin Tsai, Hwa-Lung Yu, Liang-Cheng Chang, Jun-Ru Zeng, Lingerew Nebere Kassie

“…The clay layer exhibited low resistivity (≤ 59.98 Ωm), while the sand layer displayed medium resistivity (59.98 < ρ < 136.14 Ωm), and the gravel layer is characterized by high resistivity ( ≥ 136.14 Ωm). …”
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Experimental Study on Compressive Capacity Behavior of Helical Anchors in Aeolian Sand and Optimization of Design Methods by Qingsheng Chen, Wei Liu, Linhe Li, Yijin Wu, Yi Zhang, Songzhao Qu, Yue Zhang, Fei Liu, Yonghua Guo

“…To precisely predict the compressive bearing behavior of helical anchors in aeolian sand, this study integrates in situ testing with finite element numerical analysis to systematically elucidate the non-linear evolution of its load-bearing mechanisms. The XGBoost algorithm enabled the rigorous quantification of the governing geometric features of compressive capacity, culminating in a computational framework for the bearing capacity factor (<i>N</i>_q) and lateral earth pressure coefficient (<i>K</i>_u). …”
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Exploring the association between circadian rhythms and osteoporosis: new diagnostic and therapeutic targets identified via machine learning by Jian Du, Jian Du, Tian Zhou, Ran Meng, Wei Zhang, Jin Zhou, Wei Peng

“…By comparing four machine learning algorithms, the top five genes from the SVM algorithm (ECE1, FLT3, APPL1, RAB5C and FCGR2A) were determined as key genes for OP. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

“…An additive wavelet transform (AWT) was employed to fuse unmanned aerial vehicle (UAV) multispectral imagery with Sentinel-2 data, generating enhanced spatial-spectral reflectance composites while retaining key shortwave infrared bands essential for moisture analysis. …”
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Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines by Qin Li, Nan Lin, Zuheng Wang, Yuexi Chen, Yuli Xie, Xuemei Wang, Jirui Tang, Yuling Xu, Min Xu, Na Lu, Yiqian Huang, Jiamin Luo, Zhenfang Liu, Li Jing

“…Results Among the cohort, acute myeloid leukemia (38%) was the most common HM, while gram negative bacteria (64%) were the predominant pathogens causing BSI. …”
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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…The results demonstrate that the training and prediction accuracies using the Random Forest (RF) algorithm significantly surpass those using Artificial Neural Networks (ANNs) and Support Vector Machine (SVM). …”
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