## 1 Introduction  
Intensive care unit (ICU) patients are admitted because of an acute critical illness or because of the high need for intensive continuous monitoring. In addition, critical ICU patients are prone to rapid deterioration, resulting in a possibly fatal outcome when not monitored closely. Hence, the main challenge at the ICU is to reduce the morbidity of the admitted patients and prevent mortality which has a high likelihood due to severe illness [1]. Mortality prevention requires an intensive monitoring of vital signs, such as heart and respiration rate, oxygen saturation, non-invasive or arterial blood pressure, and so forth, that can capture clinical deterioration earlier and thus improve patient outcome. In the past, multiple scoring systems have been developed (e.g., Acute Physiology, Age, Chronic Health Evaluation II, Simplified Acute Physiology Score, Sequential Organ Failure Assessment) to provide insights and even predictions regarding ICU patient mortality [2]. However, these scoring systems are population-based and often use summarised nongranular data. This calls for the need for an in-depth investigation of vital signs and associated indicators preceding any deterioration using granular continuous data. This investigation can be handled by time-series analytics to understand the behaviour and interaction of different signals. Most of the ICU mortality prediction studies focus on developing powerful mortality prediction models [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] in which the higher priority is to provide an accurate label or score about the admitted patients' status. One drawback of such an objective is paying less attention to features' simplicity and interpretability, which is the case with deep learning approaches [7; 8; 9; 10; 11; 12; 13]. The key approach in these studies is black-box modelling focusing mainly on predictive model error performance, regardless the interpretability of the features. Hence, the useful information that can be provided to the medical staff is strictly the prediction output. Moreover, a considerable number of relevant studies focus on investigating the continuously recorded vital signs of ICU patients in order to predict the mortality-risk of those patients [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. A frequently used database in these studies is the medical information mart for intensive care (MIMIC) in its three releases (MIMIC, MIMIC II and III) with different versions [17; 18]. These databases provide a diverse and very large population of ICU patients and contain high temporal resolution data including lab results, electronic documentation and bedside monitor trends and waveforms. In contrast, another approach that is used in investigating critically ill patients in the ICU is mechanistic modelling [19; 20]. Mechanistic modelling is used to describe the system from mathematical and physical dynamics perspective. The main focus of mechanistic modelling is on the system dynamics, the interaction between the different variables and the way they interact from a system perspective taking into account biological and physiological laws [21]. A mechanistic modelling approach is used in investigating biological systems by developing mathematical models [22; 23; 24]. The main focus in this presented study is to engineer features that can provide clinical insight by which the medical staff is guided through the different parameters. However, prediction accuracy is used in this study to assess the relevancy of the extracted features to the mortality events. Moreover, the dataset in our study is a low frequently measured data (i.e., hourly to bi-hourly) as it is a daily-life dataset that is not generated for research purpose. Moreover, the set of variables, parameters and the investigated population here is limited compared to the ones provided by the MIMIC databases.