The problem of optimal portfolio selection has attracted a great attention in the finance and optimization field. The future stock price should be predicted in an acceptable precision, and a suitable model and criterion for risk and the expected return of the stock portfolio should be proposed in order to solve the optimization problem. In this paper, two new criterions for the risk of stock pr...

Infant and child mortality is a health indicator of society in general, and a significant number of these deaths occurs at Neonatal Intensive Care Units. Efforts aimed at preventing infant and child mortality are effective only if based on reasonably reliable information regarding death-related factors. Information is required on risk factors leading to such outcomes in order to prioritize inte...

C R E D IT : B . S T R A U C H / C IE N C E T R A N S L A T IO N A L M E D IC IN E As one aid for treatment decisions, clinicians analyze a patient’s clinical parameters using risk assessment algorithms to predict the individual’s likelihood of developing particular diseases in the future. For example, risk prediction models for cardiovascular disease (CVD) use a person’s age, sex, systolic blo...

OBJECTIVE Risk prediction models can assist clinicians in making decisions. To boost the uptake of these models in clinical practice, it is important that end-users understand how the model works and can efficiently communicate its results. We introduce novel methods for interpretable model visualization. METHODS The proposed visualization techniques are applied to two prediction models from ...

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...

This article analyzes the simple Rescorla-Wagner learning rule from the vantage point of least squares learning theory. In particular, it suggests how measures of risk, such as prediction risk, can be used to adjust the learning constant in reinforcement learning. It argues that prediction risk is most effectively incorporated by scaling the prediction errors. This way, the learning rate needs ...