Image_3_Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017–2018.TIF by Fang Xia (146439)

Image_2_Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017–2018.TIF by Fang Xia (146439)

Image_1_Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017–2018.TIF by Fang Xia (146439)

Datasheet1_Implantable cardioverter defibrillator for primary prevention in patients with non-ischemic cardiomyopathy in the era of novel therapeutic agents- meta-analysis.docx by Yotam Kolben (15787781)

“…</p>Methods<p>We used a previous metanalysis algorithm and added an updated comprehensive literature search in PubMed for randomized control trials that examined the mortality benefit of ICD in NIDCM vs. optimal medical treatment. …”

Table 1_Assessing accuracy of BiliPredics algorithm in predicting individual bilirubin progression in neonates—results from a prospective multi-center study.docx by Britta Steffens (13231326)

“…As such, prediction tools utilizing this algorithm are expected to facilitate and safely optimize jaundice risk assessment at hospital discharge with the potential to reduce jaundice-related rehospitalizations.…”

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

“…Key predictors included hypotension, dyspnea, altered mental status, elevated leukocyte counts, and abnormal creatinine levels. A web-based risk calculator was deployed for bedside application. …”

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

“…Key predictors included hypotension, dyspnea, altered mental status, elevated leukocyte counts, and abnormal creatinine levels. A web-based risk calculator was deployed for bedside application. …”

Table 1_An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.doc by Haoran Tao (8466261)

“…</p>Conclusion<p>The XGBoost algorithm-based prediction model can be used to dynamically estimate the risk of LEA in DFU patients, making it a valuable tool for preventing the progression of DFUs to amputation.…”

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

“…Key predictors included hypotension, dyspnea, altered mental status, elevated leukocyte counts, and abnormal creatinine levels. A web-based risk calculator was deployed for bedside application. …”

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

“…Key predictors included hypotension, dyspnea, altered mental status, elevated leukocyte counts, and abnormal creatinine levels. A web-based risk calculator was deployed for bedside application. …”

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. …”

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. …”

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method by Tao Huang (110613)

“…This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application.…”

Supplementary Material for: The importance of early diagnosis and intervention in chronic kidney disease: Calls-to-action from nephrologists based mainly in Central/Eastern Europe by Covic A. (4148122)

Table_7_An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma.csv by Cong Liu (66219)

Image_3_An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma.tif by Cong Liu (66219)

Table_6_An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma.csv by Cong Liu (66219)

Table_2_An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma.csv by Cong Liu (66219)

Image_1_An Intratumor Heterogeneity-Related Signature for Predicting Prognosis, Immune Landscape, and Chemotherapy Response in Colon Adenocarcinoma.tiff by Cong Liu (66219)