## 3 Feature-Engineering
### Feature Extraction
#### 3.2.3 Physiological Features  
In order to enhance the classification performance, a manual investigation of the misclassified instances (based on the statistical and dynamic features) is required. The investigation is focusing on the false negative patients (i.e., deceased patients classified as survived) as the main objective is relevant to a reliable mortality prediction which is inversely proportional to the false negative count. This manual investigation is based on the measured physiological vital signs and uses physiological process knowledge resulting in physiological features. The different physiological features are described hereafter. By investigating the time-series of false negative patients, a consistent behaviour is noticed within the period of interest, in which the systolic blood pressure (SBP) approaches the diastolic blood pressure (DBP) as shown in Figure 5a. It is found that the difference (SBP-DBP) within certain measurement periods is smaller than 20 mmHg. A relevant observation that is noticed with other false negative patients is that this  
Figure 5: (**a**) Systolic blood pressure (BP), diastolic BP and pulse pressure (PP) of the last 150 observations (approximately the last nine days) of one false negative patient. The dashed window refers to the region where the systolic BP and diastolic BP measurements approach closely. (**b**) Systolic BP, diastolic BP and PP of the last 150 observations of another false negative patient. The mean value of the pulse pressure is 87.4 mmHg and median 88 mmHg. difference is relatively high (i.e., greater than 60 mmHg) during certain measurement periods as shown in Figure 5b. This difference between SBP and DBP is also known as the pulse pressure (_PP_) and varies normally in a range between 40-60 mmHg [30; 31]. As the _PP_ is a linear combination between two vital signs, it can be considered as a new variable from which both statistical and dynamic features can be extracted. By reviewing medical literature focusing on _PP_ and its effect on the mortality prediction (e.g., References [32; 33]), our finding is partially consistent with their conclusion. By further investigating the data, another behaviour is noticed with false negative patients, namely a frequent drop in respiration rate (_RR_) as shown in Figure 6a. Due to this behaviour, a new feature is proposed to represent this drop and the count of its occurrence. This feature is defined as the number of times the RR drops below a specific threshold within each window and is further referred as _low-RR count_. For this feature, two parameters are selected: the threshold and the window size. Both of them are searched exhaustively by maximising the classification performance by considering the new feature. The best-found combination is a threshold of 5 bpm and a window size of 60 observations. Figure 6: **(a)** Respiration rate of a deceased patient with an obvious drop at specific observations below the normal range (12–20 BPM) **(b)** Oxygen saturation (_SPO\({}_{2}\)_) of another deceased patient that drops frequently (minimum 78% during the stay). Another observation in some false negative patients' vital signs is a physiological feature related to a frequent drop of oxygen saturation \(SpO_{2}\) as shown in Figure 6b. Similar to _low-RR count_, this feature is defined as the number of times the \(SpO_{2}\) drops below a specific threshold within each window. Moreover, the threshold and window size combination affects the influence of the feature on the performance. The best-found combination is a threshold of 77% and a window size of 60. This feature is further referred to _low-\(SpO_{2}\ count\)_. Both, _low-\(SpO_{2}\ count\)_ and _low-RR count_ created only an added value to the classification performance after the fine-tuning step. Finally, a physiological feature that is imported directly from the patients' medical record is their positive and negative diagnosis with cardiovascular diseases (_CVD_).