Comparison of ROC performance before and after optimization. by Guang Tu (22054865)

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

Table_2_Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.DO... by Kuo-Yang Huang (4470730)

Table_3_Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.DO... by Kuo-Yang Huang (4470730)

Data_Sheet_1_Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived paramete... by Kuo-Yang Huang (4470730)

Table_1_Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters.DO... by Kuo-Yang Huang (4470730)

Data_Sheet_1_A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.docx by Çaǧlar Çaǧlayan (12253934)

Table_1_“What About Automated Pain Recognition for Routine Clinical Use?” A Survey of Physicians and Nursing Staff on Expectations, Requirements, and Acceptance.docx by Steffen Walter (814796)

Table_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.pdf by Michael Moor (10887888)

Data_Sheet_1_Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review.PDF by Michael Moor (10887888)

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.…”

Table_1_Predictive model for prolonged length of hospital stay in patients with osteoporotic femoral neck fracture: A 5-year retrospective study.XLSX by Worapaka Manosroi (2296279)

Presentation_1_Feasibility of Dialysate Bolus-Based Absolute Blood Volume Estimation in Maintenance Hemodialysis Patients.pdf by Simon Krenn (9874167)

Table_1_A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.pdf by Xin Sun (127355)

Table_1_Deep learning-based classification system of bacterial keratitis and fungal keratitis using anterior segment images.DOCX by Yeo Kyoung Won (15599561)

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

“…The antimicrobial stewardship algorithm (Start Smart and Then Focus) and an ASP toolkit were distributed to all intensive care unit staff. …”

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.</p><p>Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. …”

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.</p><p>Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. …”

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.</p><p>Methods: We performed a retrospective, cohort study assessing children admitted for acute respiratory failure under 24 months of age placed on HFNC in the Johns Hopkins Children's Center pediatric intensive care unit from January 2019 through January 2020. …”