Proportion of positive results in each algorithm and latent prevalences by age (a) and comorbidity (b) groups. by Jia Wei (120974)

Box plots of infection and detection time differences. by Soler Gabriela Bayolo (21011810)

Subjects:

Posterior predicted sensitivity, specificity, PPV, and NPV under Model-3 in subgroup analysis by age (a) and comorbidity (b) groups. by Jia Wei (120974)

Table 1_A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms.docx by Yi-xiang Zhang (22563236)

Interactions between individuals over three days. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of <i>η</i> and on epidemic spread. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of the quarantine adoption fraction <i>q</i> on epidemic spread. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of the estimators (blue), (orange), and the real time of infection (green). by Soler Gabriela Bayolo (21011810)

Subjects:

Comparison of infection time estimators. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of the transmission probability function on epidemic spread. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of <i>γ</i> on epidemic spread. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of super-spreaders on ranking methods. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of sensitivity and specificity on epidemic spread. by Soler Gabriela Bayolo (21011810)

Subjects:

Effect of and on epidemic spread. by Soler Gabriela Bayolo (21011810)

Subjects:

S1 File - by Xihao Shen (20347942)

“…After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). …”

6 data sets included in this study. by Xihao Shen (20347942)

“…After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). …”

Table 1_Early detection of retinal and choroidal microvascular impairments in diabetic patients with myopia.docx by Yufei Wu (3625967)

“…The AI-driven analysis revealed that decreased CVI and CT were significantly associated with age and spherical equivalent (SE), highlighting the utility of automated algorithms in identifying early microvascular impairments.…”

Predicting AMI in PTBXL. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

Patient characteristics. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”

Main results. by Axel Nyström (15220943)

“…There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. …”