Ran Yang,Dan Zhao,Chunxue Ye et al.
Ran Yang et al.
Objectives: This study aimed to develop and validate a machine learning (ML) model that integrates radiomics and conventional radiological features to predict the success of single-session extracorporeal shock wave lithotripsy (ESWL) for ureteral stones....Five machine learning models (RF, KNN, LR, SVM, AdaBoost) were developed using 10-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, and F1 score. Calibration and decision curve analyses were used to evaluate model calibration and clinical value.
Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics [0.03%]
Zhenyu Wang,Chaojun Jiang,Xianxian Zhang et al.
Zhenyu Wang et al.
Five machine learning algorithms were used to construct radiomics models (whole-brain, infarct, and combined), clinical models, and comprehensive models integrating radiomics and clinical data.
Intralesional and perilesional radiomics strategy based on different machine learning for the prediction of international society of urological pathology grade group in prostate cancer [0.03%]
Zhiping Li,Liqin Yang,Ximing Wang et al.
Zhiping Li et al.
Objective: To develop and evaluate a intralesional and perilesional radiomics strategy based on different machine learning model to differentiate International Society of Urological Pathology (ISUP) grade > 2 group and ISUP ≤ 2 prostate cancers (PCa)....Four machine learning classifiers logistic regression (LR), random forest (RF), extra trees (ET), and multilayer perceptron (MLP) were employed for model training and evaluation to select the optimal model.
Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition [0.03%]
Jiachen Ge,Jianping Cai,Gaolei Zhang et al.
Jiachen Ge et al.
Prognostic genes were identified to construct predictive models through various machine learning approaches. Following the identification of A2ML1 as a key gene, its expression profile was detected using RT-qPCR, western blotting, and immunohistochemistry....Using 23 prognostic genes, we developed and validated a prognostic signature through 101 machine learning algorithms and their combinations, with ridge regression demonstrating optimal performance. This signature demonstrated superior accuracy compared to multiple published signatures.
Uncovering key markers and therapeutic targets for renal fibrosis in diabetic kidney disease through bulk and single-cell RNA sequencing [0.03%]
Lijuan Li,Mi Tao,Xueyun Gao et al.
Lijuan Li et al.
Essential genes were subsequently confirmed by machine learning and single-cell RNA sequencing (scRNA-seq). Potential therapeutic compounds were identified by screening the ZINC database and confirmed via molecular docking.
A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making [0.03%]
Michal Pruski,Simone Willis,Kathleen Withers
Michal Pruski
Purpose: This review summarises the studies which combined Patient Reported Outcome Measures (PROMs) and Machine Learning statistical computational techniques, to predict patient post-intervention outcomes. The aim of the...
Shengpeng Li,Zhengguo Xu,Hua Diao et al.
Shengpeng Li et al.
Conclusion: Multiple machine learning models revealed that P.vulgatus may serve as a significant predictive microbe for hepatic encephalopathy after TIPS, which may be closely related to its ability to metabolize ammonia.
Progression risk of adolescent idiopathic scoliosis based on SHAP-Explained machine learning models: a multicenter retrospective study [0.03%]
Xinyi Fang,Ting Weng,Zhehao Zhang et al.
Xinyi Fang et al.
Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model [0.03%]
Jiacan Wu,Guanghong Tao,Siyuan Xie et al.
Jiacan Wu et al.
This study aimed to develop and validate a machine learning (ML) model predicting the 3-year all-cause mortality risk in HF-AF patients to support personalized risk stratification and management.
Publisher Correction: Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation [0.03%]
Seongwook Min,Jaehun An,Jae Hee Lee et al.
Seongwook Min et al.
Published Erratum
Nature reviews. Cardiology. 2025 Jul 4. DOI:10.1038/s41569-025-01189-0 2025
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