Image_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Image_5_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Table_3_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Table_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Image_1_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Image_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Image_6_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Table_2_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.docx by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

Image_4_Predicting Flow Rate Escalation for Pediatric Patients on High Flow Nasal Cannula Using Machine Learning.PDF by Joshua A. Krachman (11660266)

“…</p><p>Objective: To evaluate the abilities of tree-based machine learning algorithms to predict HFNC flow rate escalations.…”

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)

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. …”

Datasheet1_An explainable machine learning approach using contemporary UNOS data to identify patients who fail to bridge to heart transplantation.pdf by 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. …”

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.…”

Supplementary file 1_Machine learning enables early risk stratification of hymenopteran stings: evidence from a tropical multicenter cohort.docx 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 2_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.…”

Data_Sheet_1_Pre-hospital Triage of Acute Ischemic Stroke Patients—Importance of Considering More Than Two Transport Options.docx by Ludwig Schlemm (4844853)

“…Prehospital triage algorithms to determine the optimal transport destination for AIS patients with unknown vessel status have so far only considered two alternatives: the nearest comprehensive (CSC) and the nearest primary stroke center (PSC).…”

Data_Sheet_1_Pre-hospital Triage of Acute Ischemic Stroke Patients—Importance of Considering More Than Two Transport Options.docx by Ludwig Schlemm (4844853)

“…Prehospital triage algorithms to determine the optimal transport destination for AIS patients with unknown vessel status have so far only considered two alternatives: the nearest comprehensive (CSC) and the nearest primary stroke center (PSC).…”

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)

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