Table_1_A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population.DOC حسب Rujia Miao (12075653)

"…Introduction<p>An easily accessible and cost-free machine learning model based on prior probabilities of vascular aging enables an application to pinpoint high-risk populations before physical checks and optimize healthcare investment.…"

Image_2_Association Between Wait Time of Central Venous Pressure Measurement and Outcomes in Critical Patients With Acute Kidney Injury: A Retrospective Cohort Study.JPEG حسب Qilin Yang (6946559)

"…</p>Methods<p>The study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. …"

Image_3_Association Between Wait Time of Central Venous Pressure Measurement and Outcomes in Critical Patients With Acute Kidney Injury: A Retrospective Cohort Study.JPEG حسب Qilin Yang (6946559)

"…</p>Methods<p>The study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. …"

Image_1_Association Between Wait Time of Central Venous Pressure Measurement and Outcomes in Critical Patients With Acute Kidney Injury: A Retrospective Cohort Study.JPEG حسب Qilin Yang (6946559)

"…</p>Methods<p>The study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. …"

Table_1_A Phenotyping of Diastolic Function by Machine Learning Improves Prediction of Clinical Outcomes in Heart Failure.DOCX حسب Haruka Kameshima (11870333)

"…During a mean follow-up period of 2.6 ± 2.0 years, 62 patients (22%) experienced the primary endpoint. Cluster-based classification predicted events with a hazard ratio 1.68 (p = 0.019) that was independent from and incremental to the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score for HF, and from left ventricular end-diastolic volume and global longitudinal strain, whereas guidelines-based classification did not retain its independent prognostic value (hazard ratio = 1.25, p = 0.202).…"

Datasheet1_An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation.pdf حسب Mamoun T. Mardini (14672933)

"…Out of them, 12% had primary outcomes indicating Status 2 failure. Our ML models were based on 19 variables from the UNOS data. …"

DataSheet_1_Potential Impact of Rapid Multiplex PCR on Antimicrobial Therapy Guidance for Ventilated Hospital-Acquired Pneumonia in Critically Ill Patients, A Prospective Observati... حسب Florian Guillotin (9298877)

"…Antimicrobial therapies based on FAPP results were simulated by independent blinded experts according to a predefined algorithm and compared to 1) those prescribed in practice according to local guidelines (real-life), and 2) those that complied with the international ERS/ESICM/ESCMID/ALAT recommendations. …"

Data Sheet 1_Triglyceride-glucose index and mortality in congestive heart failure with diabetes: a machine learning predictive model.doc حسب Lin Yu (221619)

"…Using restricted cubic spline analysis, a linear link between the TyG index and mortality rates was found, indicating that a rise in TyG correlates with a heightened risk of unfavorable outcomes. The predictive performance was evaluated using seven machine learning algorithms, with the Random Survival Forest (RSF) algorithm achieving the best performance (AUC=0.817).…"

datasheet1_Evaluation of a Multidisciplinary Antimicrobial Stewardship Program in a Saudi Critical Care Unit: A Quasi-Experimental Study.docx حسب Abdul Haseeb (5830565)

"…<p>Background: Antimicrobial stewardship programs (ASPs) are collaborative efforts to optimize antimicrobial use in healthcare institutions through evidence-based quality improvement strategies. …"

Table 1_Identification of routine blood derived hematological and lipid indices in ILD through machine learning; a retrospective case-control study.docx حسب Lichen Zhu (22399222)

"…The random forest model outperformed seven other machine learning approaches, with AUC values of 0.868 (validation set), 0.885 (test set), and 0.849 (external cohort), demonstrating consistent predictive accuracy.</p>Discussion<p>Based on these findings, we developed an online prediction tool to assist primary care clinicians in assessing the risk of ILD in suspected cases. …"

Image 1_Identification of routine blood derived hematological and lipid indices in ILD through machine learning; a retrospective case-control study.tif حسب Lichen Zhu (22399222)

"…The random forest model outperformed seven other machine learning approaches, with AUC values of 0.868 (validation set), 0.885 (test set), and 0.849 (external cohort), demonstrating consistent predictive accuracy.</p>Discussion<p>Based on these findings, we developed an online prediction tool to assist primary care clinicians in assessing the risk of ILD in suspected cases. …"

Data_Sheet_1_Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.docx حسب Qunfeng Niu (13263975)

"…By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. …"

Image 1_Development of machine learning predictive model for type 2 diabetic retinopathy using the triglyceride-glucose index explained by SHAP method.png حسب Xiaoqin Liu (296429)

"…TyG provides a cost-effective alternative to conventional IR biomarkers (e.g., HOMA-IR), enabling practical DR risk stratification in primary care.</p>…"

Image 2_Development of machine learning predictive model for type 2 diabetic retinopathy using the triglyceride-glucose index explained by SHAP method.png حسب Xiaoqin Liu (296429)

"…TyG provides a cost-effective alternative to conventional IR biomarkers (e.g., HOMA-IR), enabling practical DR risk stratification in primary care.</p>…"

2000–2020 Monthly Air Quality Index (AQI) Dataset of China حسب Chaohao Ling (19840471)

"…Four tree-based ensemble algorithms (Random Forest [RF], Gradient Boosting Machine [GBM], CatBoost, XGBoost) were compared, with the RF model selected as optimal (test set: R² = 0.83, Root Mean Square Error [RMSE] = 10.25, Mean Absolute Error [MAE] = 9.03) after validation via 10-fold geographic stratified cross-validation and 100 bootstrap iterations; Recursive Feature Elimination (RFE) further refined 14 core predictors to minimize overfitting. …"

Image 1_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif حسب Yunlin Yang (10277429)

"…Patients were categorized into metformin and non-metformin groups based on medication exposure. Primary outcomes were in-hospital and ICU mortality, while 30-day and 90-day all-cause mortality served as secondary endpoints. …"

Image 2_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif حسب Yunlin Yang (10277429)

"…Patients were categorized into metformin and non-metformin groups based on medication exposure. Primary outcomes were in-hospital and ICU mortality, while 30-day and 90-day all-cause mortality served as secondary endpoints. …"

Image1_Adverse events related to neuromuscular blocking agents: a disproportionality analysis of the FDA adverse event reporting system.pdf حسب Liangxia Li (17664248)

"…A positive signal was generated when both algorithms show an association between the target drug and the AE.…"

Table1_Adverse events related to neuromuscular blocking agents: a disproportionality analysis of the FDA adverse event reporting system.xlsx حسب Liangxia Li (17664248)

"…A positive signal was generated when both algorithms show an association between the target drug and the AE.…"