Data_Sheet_1_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.docx by Qin-Yu Zhao (10014626)

Data_Sheet_2_A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.xlsx by Qin-Yu Zhao (10014626)

Table_1_Development and validation of a multivariable nomogram predictive of hepatitis B e antigen seroconversion after pregnancy in hepatitis B virus-infected mothers.DOCX by Wenting Zhong (11460046)

The study flowchart. by Nguyen Tat Thanh (10296398)

“…These findings support its utility as a practical and accessible tool for early risk stratification in DSS patients. These results support the use of LAR as a practical and accessible tool for risk stratification in pediatric dengue care.…”

Missing value chart of candidate variables. by Nguyen Tat Thanh (10296398)

“…These findings support its utility as a practical and accessible tool for early risk stratification in DSS patients. These results support the use of LAR as a practical and accessible tool for risk stratification in pediatric dengue care.…”

Data Sheet 1_Association between admission Braden Skin Score and delirium in surgical intensive care patients: an analysis of the MIMIC-IV database.docx by Meiling Shang (21086624)

“…The primary outcome was incidence of delirium. Feature importance of BSS was initially assessed using a machine learning algorithm, while restricted cubic spline (RCS) models and multivariable logistic analysis evaluated the relationship between BSS and delirium. …”

Table 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

“…We also identified total saturated fat, polyunsaturated fatty acid, vitamin A, β carotene, vitamin B2, and iron are the primary dietary factors associated with FI. Based on these dietary factors, we developed a novel FI risk prediction model. …”

Image 1_Association between pro-inflammatory diet and fecal incontinence: a large population-based study.pdf by Haiyang Wang (22389)

“…We also identified total saturated fat, polyunsaturated fatty acid, vitamin A, β carotene, vitamin B2, and iron are the primary dietary factors associated with FI. Based on these dietary factors, we developed a novel FI risk prediction model. …”

Data_Sheet_1_A machine learning based radiomics approach for predicting No. 14v station lymph node metastasis in gastric cancer.docx by Tingting Ma (228102)

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

“…Feature selection was performed using LASSO regression, and predictive modeling was carried out using machine learning algorithms.…”

Data_Sheet_1_A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration ana... by Zuguang Chen (19743121)

Table_1_An Online Pre-procedural Nomogram for the Prediction of Contrast-Associated Acute Kidney Injury in Patients Undergoing Coronary Angiography.pdf by Duanbin Li (11744513)

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

“…Introduction<p>This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG). …”

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

“…Introduction<p>This study aimed to develop a diabetic retinopathy (DR) Prediction model using various machine learning algorithms incorporating the novel predictor Triglyceride-glucose index (TyG). …”

Image_1_A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population.JPEG by 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.…”

Table_1_A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population.DOC by 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.…”

DATASET AI by Elena Stamate (18836305)

“…</p><p dir="ltr">The primary aim of this dataset is to enable the development and validation of machine learning models for:</p><ul><li>Early identification of STEMI patients at high risk of developing cardiogenic shock;</li><li>Clinical triage optimization and prioritization for urgent angiography;</li><li>Supporting time-sensitive decision-making in resource-limited or overcrowded emergency settings.…”

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 by Qilin Yang (6946559)

“…</p>Results<p>Twenty Nine Thousand and Three Hundred Thirty Six patients with AKI were enrolled, and the risk of in-hospital mortality increased when the CVP acquisition time was >9 h in the Cox proportional hazards regression model. …”

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 by Qilin Yang (6946559)

“…</p>Results<p>Twenty Nine Thousand and Three Hundred Thirty Six patients with AKI were enrolled, and the risk of in-hospital mortality increased when the CVP acquisition time was >9 h in the Cox proportional hazards regression model. …”

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 by Qilin Yang (6946559)

“…</p>Results<p>Twenty Nine Thousand and Three Hundred Thirty Six patients with AKI were enrolled, and the risk of in-hospital mortality increased when the CVP acquisition time was >9 h in the Cox proportional hazards regression model. …”