Before revision:  Our comprehensive analysisof a multi-study patient cohort evaluates HO for three different classifier types and over a range of different optimization settings. Consistent with previous research, we find that Bayesian optimization is more efficient than grid search or random sampling-based methods , identifying promising classifiers with fewer evaluated hyperparameter configurations.
After revision:  Our comprehensive analysisof a multi-study patient cohort with our choices of dataset partitioning and hyperparameter optimization objective as well as assessing selected classifiers in external (as well as internal) validation. We find that classifiers selected by Bayesian optimization for in-hospital mortality can outperform those selected by grid search or random sampling. However, in contrast to previous research: 1) Bayesian optimization is not more efficient in selecting classifiers in all instances compared to grid search or random sampling-based methods , identifying promising classifiers with fewer evaluated hyperparameter configurations.