The algorithm for the same-day surgery protocol. by Magnus Tveit (11442200)

A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS₂‑Based Microparticles in a Breast Cancer Mode... by Maria Kanelli (5494031)

“…MoS₂ nanosheets exhibit high photothermal conversion efficiency and require low-power laser irradiation. A machine learning algorithm was applied to acquire the optimal laser operation conditions. …”

Study cohort selection. by Srilekha Sridhara (15374444)

“…The study cohorts were 1:1 propensity matched without replacement across 26 covariates using an optimal matching algorithm that minimizes the sum of absolute pairwise distance across the matched sample after fitting and using logistic regression as the distance function. …”

Image_3_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients... by Mingxue Yu (10273496)

Image_4_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients... by Mingxue Yu (10273496)

Image_5_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients... by Mingxue Yu (10273496)

Image_2_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients... by Mingxue Yu (10273496)

Image_1_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Patients... by Mingxue Yu (10273496)

Data_Sheet_1_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Pat... by Mingxue Yu (10273496)

Data_Sheet_2_Development and Validation of a Novel Risk Prediction Model Using Recursive Feature Elimination Algorithm for Acute-on-Chronic Liver Failure in Chronic Hepatitis B Pat... by Mingxue Yu (10273496)

Table_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf by Jenish Maharjan (11998331)

“…</p>Results<p>After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. …”

Image_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf by Jenish Maharjan (11998331)

“…</p>Results<p>After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. …”

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 bilirubin values usually peak few days after hospital discharge, jaundice remains a leading cause of rehospitalization. …”

Data_Sheet_1_Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19.docx by Xiao-Hui Wang (1762348)

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)

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>Methods<p>The study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. The primary outcome was in-hospital mortality. Secondary outcomes included the number of ICU-free days and norepinephrine-free days at 28 days after ICU admission, and total fluid input and fluid balance during the first and second day. …”

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>Methods<p>The study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. The primary outcome was in-hospital mortality. Secondary outcomes included the number of ICU-free days and norepinephrine-free days at 28 days after ICU admission, and total fluid input and fluid balance during the first and second day. …”

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>Methods<p>The study was based on the Medical Information Mart for Intensive Care (MIMIC) IV database. The primary outcome was in-hospital mortality. Secondary outcomes included the number of ICU-free days and norepinephrine-free days at 28 days after ICU admission, and total fluid input and fluid balance during the first and second day. …”

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

“…Prospective audit and feedback, in addition to prescribing forms for common infectious diseases and education, were the primary antimicrobial strategies.</p><p>Results: We found that the mean total monthly antimicrobial consumption measured as defined daily dose per 100 bed days was reduced by 25% (742.86 vs. 555.33; p = 0.110) compared to 7% in the control condition (tracer medications) (35.35 vs. 38.10; p = 0.735). …”