## 1 Introduction
In the light of the given approaches (Black-box predictive models and mechanistic models) and reviewed studies, our study stands between the two approaches (i.e., pure black-box modelling and mechanistic modelling), as the main focus of the study is to achieve an efficient and informative set of physiologically meaningful features (mechanistic aspect) by means of enhancing the predictive model error performance (black-box aspect) that could be representable for European ICU departments. From an analytical perspective, the series of recordings for each vital sign is considered a time-series that is sampled by a specific sampling rate. During ICU monitoring, different vital signs are measured and recorded simultaneously, in which the simultaneity facilitates studying correlation, interaction and behaviour between and within the different vital signs. Moreover, the time-series of recorded vital signs enable extraction of different features (typically statistical and dynamic) within segmented time windows, showing the dynamic behaviour of the recorded sign. Many features can be extracted within consecutive or overlapping time windows for different vital signs, either individually or in combination. This option provides a large number of dimensions that have to be evaluated and adjusted to inform the decision making of the algorithm, which requiresan exhaustive investigation. However, such an investigation including a large number of numerical features is not an easy task for medical experts. Due to the high dimensionality issue, it is required to conduct such an investigation via a computational algorithm. In order to cope with these challenges, a simple and powerful classifier is used to explore the features. Ideally, this classifier should handle the problem of classification intuitively with the optimal margin hypothesis [25] which maximises the separability between the different classes. Moreover, the classifier should be capable of dealing with high dimensional data efficiently. The proposed classifier for this purpose is the linear hard margin support vector machine (SVM) classifier which represents the simplest version of the powerful SVMs. The reason for using SVMs that it is relying on the maximum margin hypothesis. For linear hard margin SVM, it restrictively works efficiently once the input features provide linearly separable data points. With this property, it is feasible to extract features that may have a medical interpretation or physiological ground as the classifier would deal with the features as they are presented in the input space. In other words, it is required to have an acceptable performance only if the data points in the presented feature space are linearly separable with minimum misclassification error [25; 26]. This error intolerance (or minimum tolerance) ensures that the introduced features provide a clear separation between the different classes (i.e., mortality and survival). Moreover, utilising such a linear classifier controls the dimensionality of the solution as it would only find a solution in the introduced dimensions. In other words, using a more sophisticated classifier (e.g., Radial Basis Function (RBF) SVM) would find a solution in an uncontrolled dimensionality, for instance, RBF SVM reaches infinite dimensionality due to the characteristics of the Gaussian kernel [26]. In this study, the problem is presented as integration between time-series prediction and classification. This integration is obtained by extracting features from the time-series and considering the dynamic behaviour of the time-series to construct the input space of the model. On the other hand, the output of the model is represented by the labels mortality/survival. The prediction is obtained by predicting the state (label) after the final record (last moment at ICU) on average 1.5 days ahead. The final record is the record preceding the patient's death (mortality label) or transfer to a lower care ward (survival label).