Côte d’Ivoire model outputs on actual and optimized 2016 spending and impact by care delivery platform. by Nicole Fraser-Hurt (2636833)

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Armenia model outputs on optimized resource allocation and health impact by EUHC care package (2019 vs. higher budget scenario). by Nicole Fraser-Hurt (2636833)

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Data_Sheet_1_Adaptive restraint design for a diverse population through machine learning.pdf by Wenbo Sun (531308)

Zimbabwe model outputs on actual and optimized 2016 spending and impact by intervention. by Nicole Fraser-Hurt (2636833)

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Data_Sheet_1_Optimizing clinical outcomes in polypharmacy through poly-de-prescribing: a longitudinal study.docx by Doron Garfinkel (18465243)

Key data on the three country case studies. by Nicole Fraser-Hurt (2636833)

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Overview of the five stages of the HIPtool application. by Nicole Fraser-Hurt (2636833)

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Image_1_The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.TIF by Xiaoying Lou (9700261)

Image_2_The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.TIF by Xiaoying Lou (9700261)

Data_Sheet_1_Phase-wise evaluation and optimization of non-pharmaceutical interventions to contain the COVID-19 pandemic in the U.S..pdf by Xiao Zhou (122661)

Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP by Xiaoyuan Wang (492534)

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

Data_Sheet_3_Development of the Interdisciplinary and Interprofessional Course Concept “Advanced Critical Illness Life Support”.PDF by Mark Michael (11696493)

Data_Sheet_2_Development of the Interdisciplinary and Interprofessional Course Concept “Advanced Critical Illness Life Support”.PDF by Mark Michael (11696493)

Data_Sheet_1_Development of the Interdisciplinary and Interprofessional Course Concept “Advanced Critical Illness Life Support”.PDF by Mark Michael (11696493)

Table_1_Development of the Interdisciplinary and Interprofessional Course Concept “Advanced Critical Illness Life Support”.DOCX by Mark Michael (11696493)

Data_Sheet_1_Performance of TB-LAMP in the Diagnosis of Tuberculous Empyema Using Samples Obtained From Pleural Decortication.docx by Chang Liu (35901)

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

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

“…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”

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

“…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”