Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease by Ruijie Zhao, Yun Wang, Jiaru Wang, Zixing Wang, Ran Xiao, Ying Ming, Sirong Piao, Jinhua Wang, Lan Song, Yinghao Xu, Zhuangfei Ma, Peilin Fan, Xin Sui, Wei Song

“…Conclusion With 63.7% reduction of radiation dose, the overall image quality of LDCT DLR was comparable to HRCT HIR in prone scanning for early ILD patients. …”
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Microstate Co-Occurrence Matrices-Based Brain Activity Pattern Changes Analysis on Chronic Tinnitus Subjects by Yingying Wang, Gui Yang, Hongyu Liu, Xin Huang, Shuqing Han, Zhixiang Gu, Liyang Shao, Lei Zhang, Yuan Tao

“…Unlike healthy individuals, tinnitus patients exhibit minimal changes between OE and CE, indicating the reduction of brain flexibility. This study advances EEG microstate analysis by integrating co-occurrence matrix algorithms, offering a direct and comprehensive assessment of microstate stability and interactions. …”
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Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods by Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao

“…Conclusions This study developed an optimized mapping algorithm to predict the utility index from the QLQ-C30 QLQ-LC13 to the SF-6D. …”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

“…Conclusion The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. …”
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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

“…Venant’s system of equations is built to generate the feature dataset, which is then combined with the random forest algorithm to create a nonlinear prediction model. An example analysis demonstrates that the optimal feedforward time of the open channel gate group is negatively connected with the flow condition and that the method can manage the water distribution error within 13.97% and the water level error within 13%. …”
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Advanced health state intelligent diagnosis of lithium-ion batteries based on CNN-WNN-WBiLSTM model with attention mechanism by Walid Mchara, Mohamed Abdellatif Khalfa, Lazhar Manai

“…Our results underscore the efficacy of our algorithm in Li-ion battery health management, offering higher prediction accuracy and precise multi-step SOH prediction compared to other neural network methods.…”
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An analysis of the relationship of triglyceride glucose index with diffuse large B-cell lymphoma prognosis: a retrospective study by QingQing Luo, Zhixiang Lei, Haizhou Miao, Ting Huang, Li Yu

“…Calibration curves showed good agreement between the nomogram predictions and actual outcomes, and DCA highlighted the high clinical utility of the model.ConclusionThe TyG index is an independent prognostic factor in DLBCL patients, and the TyG-based nomogram model provides enhanced predictive accuracy compared to the IPI. …”
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A population spatialization method based on the integration of feature selection and an improved random forest model. by Zhen Zhao, Hongmei Guo, Xueli Jiang, Ying Zhang, Changjiang Lu, Can Zhang, Zonghang He

“…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …”
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AI-driven multi-omics profiling of sepsis immunity in the digestive system by Yuan Gao, Hong Chen, Ruolan Wu, ZuJun Zhou

“…It focuses on the synthesis of clinical biomarkers of sepsis and parameters related to the gut microenvironment with the help of artificial intelligence, enabling marker identification, immunostratification and predictive modeling. This feasible clinical decision-making algorithm based on “combinatorial typing” is an important tool for realizing precision medicine for sepsis patients.…”
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Dynamic Modeling and Composite Attitude Cooperative Control for Dumbbell-Shaped Spacecraft by Qingmu Ai, Yingzi Guan, Yuzhi Wang, Shunli Li

“…Then, leveraging graph and consensus theories, an attitude coordination consistency algorithm is developed. A virtual leader is designed using model predictive control. …”
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Forecasting Chlorophyll-a in the Murray–Darling Basin Using Remote Sensing by Ming Li, Klaus Joehnk, Peter Toscas, Luis Riera Garcia, Huidong Jin, Tapas K. Biswas

“…The prediction intervals generally aligned well with nominal levels, demonstrating their reliability. …”
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Inverse Modeling for Subsurface Flow Based on Deep Learning Surrogates and Active Learning Strategies by Nanzhe Wang, Haibin Chang, Dongxiao Zhang

“…Abstract Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial to characterize underground geologic properties and reduce prediction uncertainty. …”
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DiffBTS: A Lightweight Diffusion Model for 3D Multimodal Brain Tumor Segmentation by Zuxin Nie, Jiahong Yang, Chengxuan Li, Yaqin Wang, Jun Tang

“…This design prevents computational complexity from growing in square steps. Prediction accuracy and model stability are crucial in brain tumor segmentation; we propose the Edge-Blurring Guided (EBG) algorithm, which directs the diffusion model to focus more on the accuracy of segmentation boundaries during the iterative sampling process. …”
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Optimization of Fused Deposition Modeling Parameters for Mechanical Properties of Polylactic Acid Parts Based on Kriging and Cuckoo Search by Yuan Yang, Yiyang Wang, Bowen Xue, Changxu Wang, Bo Yang

“…Secondly, a Kriging-based prediction model for mechanical properties was constructed by learning sample data, and the nonlinear mapping relationship between process parameters and tensile strength was obtained. …”
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Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer by Man Wu, Wanyi Feng, Xinya Li, Yunan Liu, Chuxin Cao

“…Therefore, in order to improve prediction accuracy, this study designs a short-term power load forecasting model integrating multi-scale GCN and the improved Transformer, as well as the prediction method based on this model. …”
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Frequency-Domain Finite Element Modeling of Seismic Wave Propagation Under Different Boundary Conditions by Ying Zhang, Haiyang Liu, Shikun Dai, Herui Zhang

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Centrality nearest-neighbor projected-distance regression (C-NPDR) feature selection for correlation-based predictors with application to resting-state fMRI study of major depressi... by Elizabeth Kresock, Bryan Dawkins, Henry Luttbeg, Yijie Jamie Li, Rayus Kuplicki, B A McKinney

“…<h4>Background</h4>Nearest-neighbor projected-distance regression (NPDR) is a metric-based machine learning feature selection algorithm that uses distances between samples and projected differences between variables to identify variables or features that may interact to affect the prediction of complex outcomes. …”
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ICD-10-AM codes for cirrhosis and related complications: key performance considerations for population and healthcare studies by Kelly L Hayward, Amy L Johnson, Benjamin J Mckillen, Vikas Bansal, Leigh U Horsfall, Gunter Hartel, Chris Moser, Elizabeth E Powell, Patricia C Valery

“…Accuracy of individual codes and grouped combinations was determined by calculating sensitivity, positive predictive value (PPV), negative predictive value and Cohen’s kappa coefficient (κ).Results The PPVs for ‘grouped cirrhosis’ codes (0.96), hepatocellular carcinoma (0.97) ascites (0.97) and ‘grouped varices’ (0.95) were good (κ all &gt;0.60). …”
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Trabecular bone score in rheumatic disease by M. V. Kozyreva, O. A. Nikitinskaya, N. V. Toroptsova

“…The data obtained indicate that TBS predicts fractures better in RD, especially in patients receiving glucocorticoids, than BMD or the FRAX algorithm. …”
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“Can computed tomographic angiography” solve the puzzle of complexities in the pediatric complex congenital heart disease’s by Javaid Iqbal, Sheikh Riaz Rasool, Tariq Ahmad Gojwari, Naseer Ahmad Choh, Moonisah Rafiq

“…The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for diagnosing CHD were 95.83%, 100%, 100%, 99.35%, and 99.44% for CTA and 76.96%, 99.78%, 98.24%, 96.51%, and 96.69% for TTE respectively. …”
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