## 6 Conclusions  
In this study, we proposed a proof of concept for a feature engineering approach to explore features that can provide clinical insight in order to enhance the mortality prediction of ICU patients using the machine learning algorithm of linear hard margin SVM. The optimal combination of features that provided the best classification performance comprises the following features:  
1. Statistical features of the raw physiological variables, their first derivative of \(SBP\), \(DBP\), \(MAP\), \(RR\), \(SpO_{2}\) and \(PP\). Moreover, the statistical features extracted from the EWS of \(SBP\), \(RR\) and \(SpO_{2}\). A window size of 15 observations. 2. Real and absolute values of _correlation coefficients_ between \(HR\) and \(SBP\) in a window size of 30 observations. 3. _Outlier-occurrence count_ of \(SBP\), \(MAP\), \(RR\) and \(PP\). represented in an aggregate form except for the \(SBP\) represented individually as well. A window size of 60 observations. 4. _crossing-the-mean count_ of \(SBP\) and \(RR\), it is presented in the aggregate form. A window size of 60 observations. 5. _Outlier indicator_ of \(SBP\) and \(DBP\). A window size of 60 observations. 6. _Low-\(SpO_{2}\) count_ less than 77% and _low-\(RR\) count_ less than 5 \(BPM\). A window size of 60 observations. The proposed approach allows moving from black-box to grey-box modelling, starting from a powerful black-box technique such as SVMs. Moreover, in this case study, low frequently measured vital signs (hourly to bi-hourly) enabled us to extract efficient features for the purpose of relatively long term analysis. From a feature engineering perspective, some features or variables are individually unable to distinguish between the two classes (i.e., mortality and survival). However, by combining such features in suitable feature combinations, their use becomes beneficial. Furthermore, combining different efficient features might cause a drop in performance. Therefore, a feature fine-tuning phase is essential in order to synthesise efficient feature-combination. From the medical perspective, we can conclude that the heart rate as an individual variable can be confusing to predict the mortality. This conclusion is supported by improving the error performance by excluding the heart rate features. Moreover, we can recommend paying more attention to the pulse pressure explicitly, either high or low level, since both levels are found associated with the mortality of a group of patients. Watching the pulse pressure requires implicitly to consider the diastolic blood pressure which is excluded from the EWS standards. Finally, we conclude that different profiles of patients require a different set of features to handle the mortality prediction efficiently. For future work, we propose to test the developed model with the extracted features along the stay of the ICU patients. In other words, we can scan the complete period of stay with the moving window of 60 observations for feature extraction to predict the mortality-risk 24 observations ahead. Despite the fact that along the stay the patients will be labelled as survival, the medical doctors may label any upcoming events with possible mortality-risk. Conceptualization, A.Y.A.A., P.V., S.L., B.V. and J.-M.A. ; Data curation, J.V., F.W. and V.S. ; Formal analysis, A.Y.A.A. ; Investigation, A.Y.A.A. ; Methodology, A.Y.A.A. ; Resources, D.M. and P.V. ; Software, A.Y.A.A. ; Supervision, S.L., B.V. and J.-M.A. ; Validation, A.Y.A.A. ; Visualization, A.Y.A.A., J.V. ; Writing--original draft, A.Y.A.A. ; Writing--review and editing, A.Y.A.A., J.V., S.L., B.V. and J.-M.A. This research is funded by a European Union Grant through wearIT4 health project. The wearIT4 health project is being carried out within the context of the Interreg V-A Euregio Meuse-Rhine programme, with EUR 2,3 million coming from the European Regional Development Fund (ERDF).