Table_1_Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.X... by Fang Tian (360682)

Comparison of VCUG indication algorithms: Risk stratification criteria and predictive performance for therapeutic intervention. by Tomohiro Inoguchi (22552798)

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_3_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data_Sheet_4_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data_Sheet_2_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data_Sheet_1_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data Sheet 1_TBESO-BP: an improved regression model for predicting subclinical mastitis.pdf by Kexin Han (10933209)

Table_1_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX by Ranran Chen (3308463)

Table_2_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX by Ranran Chen (3308463)

Table_2_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX by Ranran Chen (3308463)

Table_1_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX by Ranran Chen (3308463)

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)

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)

“…</p>Methods<p>This retrospective observational cohort study used data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The primary outcome was incidence of delirium. …”

Image 1_Correlation between metformin use and mortality in acute respiratory failure: a retrospective ICU cohort study.tif by 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 by 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. …”

The study flowchart. by Nguyen Tat Thanh (10296398)

“…At admission, LAR demonstrated superior discriminatory ability (AUC: 0.82; 95% CI: 0.76–0.87) compared to serum lactate (AUC: 0.72; 95% CI: 0.65–0.78) and LB ratio (AUC: 0.68; 95% CI: 0.62–0.74) (all p < 0.001). The Youden-index based optimal LAR cutoff was 1.25, whereas that for the LB ratio was 0.20. …”

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

“…At admission, LAR demonstrated superior discriminatory ability (AUC: 0.82; 95% CI: 0.76–0.87) compared to serum lactate (AUC: 0.72; 95% CI: 0.65–0.78) and LB ratio (AUC: 0.68; 95% CI: 0.62–0.74) (all p < 0.001). The Youden-index based optimal LAR cutoff was 1.25, whereas that for the LB ratio was 0.20. …”

Supplementary file 2_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.xlsx by Feng Han (10919)

“…Background<p>Hymenopteran stings (from bees, wasps, and hornets) can trigger severe systemic reactions, especially in tropical regions, risking patient safety and emergency care efficiency. Accurate early risk stratification is essential to guide timely intervention.…”

Image 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.png by Feng Han (10919)

“…Background<p>Hymenopteran stings (from bees, wasps, and hornets) can trigger severe systemic reactions, especially in tropical regions, risking patient safety and emergency care efficiency. Accurate early risk stratification is essential to guide timely intervention.…”