ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the fullPIERS model. by Guiyou Yang (10259597)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger tre</p>…”

Characteristics of women at admission. by Guiyou Yang (10259597)

“…The PIERS-ML model’s AUC-PRC peaked on day 0 (0.65), and notably decreased thereafter. …”

Effect of oral potassium supplementation on urinary potassium excretion and its diagnostic value for primary aldosteronism by Ke Zhang (115386)

“…Post-supplementation urinary potassium has greater diagnostic value for distinguishing PA than pre-supplementation indicators.…”

AUC statistics comparing statistical trends in control and test populations. by Madeline Jarvis-Cross (22394247)

“…<p>To evaluate statistical trends, we calculated Kendall’s rank correlation coefficient during the pre-critical interval (here, days one to sixty), and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger trends. …”

Comparison of the performance of the models used. by Tiago de Oliveira Barreto (20485207)

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

Data profile from the RegulaRN Leitos Gerais. by Tiago de Oliveira Barreto (20485207)

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

Description of database. by Tiago de Oliveira Barreto (20485207)

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

Metrics obtained by the computer models. by Tiago de Oliveira Barreto (20485207)

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

Feature importance of MLP models. by Tiago de Oliveira Barreto (20485207)

“…The results evidenced which models could adequately assist medical regulators during the decision-making process for bed regulation, enabling even more effective regulation and, consequently, greater availability of beds and a decrease in waiting time for patients.…”

Data Sheet 1_Deep learning-based image reconstruction benefits diffusion tensor imaging for assessing severity of depression.docx by Yuanyuan Cui (276987)

“…Using DLR DTI, decreases in FA values were observed in the right CST, right anterior thalamic radiation, and left superior longitudinal fasciculus. …”

Attention reduces decision uncertainty under high cognitive demand. by Rahul Garg (3064578)

“…Positive values on the <i>x</i>-axis indicate greater selectivity for CS+ odors. …”

Supplementary Material for: Exploring the ability of novel choroidal biomarkers in predicting treatment outcomes of polypoidal choroidal vasculopathy by Zhang Y. (2828780)

“…Using ROC curves, we found that the CVI value demonstrated superior predictive ability compared to the CT value, with AUC of 0.842 and the best cut-off value of 0.445. …”

Table 1_Elevated NLR and PCT levels and reduced GCS score predict 90-day mortality in heatstroke: findings from a 13-year retrospective cohort study.docx by Huili Guo (6577373)

“…</p>Conclusion<p>An NLR greater than 11 in the early phase could be an independent predictor of prognosis in heatstroke patients, and an increased PCT level and decreased GCS score were risk factors for a poor prognosis.…”