Table_1_Optimal Reopening Pathways With COVID-19 Vaccine Rollout and Emerging Variants of Concern.pdf by Yanyu Xiao (682443)

Image_1_SARS-CoV-2 Diagnostic Tests: Algorithm and Field Evaluation From the Near Patient Testing to the Automated Diagnostic Platform.PDF by Nicolas Yin (8815997)

Table_1_SARS-CoV-2 Diagnostic Tests: Algorithm and Field Evaluation From the Near Patient Testing to the Automated Diagnostic Platform.PDF by Nicolas Yin (8815997)

Table_2_SARS-CoV-2 Diagnostic Tests: Algorithm and Field Evaluation From the Near Patient Testing to the Automated Diagnostic Platform.PDF by Nicolas Yin (8815997)

Image_2_SARS-CoV-2 Diagnostic Tests: Algorithm and Field Evaluation From the Near Patient Testing to the Automated Diagnostic Platform.PDF by Nicolas Yin (8815997)

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)

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)

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

Study timeline. by Wai Yan Min Htike (20442333)

“…<div><p>Objective</p><p>This study will evaluate the feasibility and preliminary effectiveness of the COACH (Community Health Worker Optimization of Antihypertensive Care in HIV) intervention, which integrates hypertension management into existing HIV care for people living with HIV (PLWH) in Tanzania.…”

Table_1_Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.DOCX by Arto J. Hautala (3072576)

“…The ML tools may help decision-making when planning the optimal allocation of health care resources.</p>…”

Table_1_Deep-STP: a deep learning-based approach to predict snake toxin proteins by using word embeddings.DOCX by Hasan Zulfiqar (12117255)

Table 1_The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.docx by Zhou Liu (1506679)

“…Background<p>Acute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), with a mortality rate ranging from 15 to 20%. …”