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Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Predicting COVID-19 Vaccination Uptake Using a Small and Interpretable Set of Judgment and Demographic Variables: Cross-Sectional Cognitive Science Study

Both operant keypress and rating tasks measure variables that quantify the average (mean) magnitude (K), variance (σ), and pattern (ie, Shannon entropy [H]) of reward and aversion judgments [35]. We refer to this methodology and the multiple relationships between these variables and features based on their graphical relationships as relative preference theory (RPT; Figure 1) [18,36].

Nicole L Vike, Sumra Bari, Leandros Stefanopoulos, Shamal Lalvani, Byoung Woo Kim, Nicos Maglaveras, Martin Block, Hans C Breiter, Aggelos K Katsaggelos

JMIR Public Health Surveill 2024;10:e47979

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

Moreover, random data splitting can introduce bias; thus, k-fold cross-validation or leave-one-out cross-validation strategies are preferred when training DL models. In addition, it is important that different sets (ie, training, validation, and testing) contain different patients, also known as interpatient data splitting, so that the study’s results are more reliable.

Georgios Petmezas, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A Rogers, Aggelos K Katsaggelos, Nicos Maglaveras

JMIR Med Inform 2022;10(8):e38454