## 3 Results and Discussion
### Multivariate Data Analysis
#### 3.2.1 Comparisons Between Groups A and B  
Based on all the three datasets, t-SNE plots showed data patterns according to the separation of samples between Groups A and B (Figure 4a-c). This indicates that the used data could potentially be associated with disease severity in the ICU, and in this case, with the need for IMV in Group B. Interestingly, a better separation among groups was achieved when considering all 34 features (Figure 4c). Despite this apparent separation between samples of Groups A and B, the developed Naive Bayes models with the entire datasets presented poor performance, with AUCs lower than 0.588, independently of being based only on the cytokine results, the routine blood analyses, or both (Table 4). To improve the performance of these predictive models, an information gain algorithm was implemented to determine the 10 features that contributed most to predicting the outcome. New models were then developed by sequentially removing features with the least information gain. This strategy resulted in much better models (Table 4). For example, a very good model was obtained with the following six cytokines: IL-1 RN, CXCL10, CCL3, CXCL9, IL-7, HGF (AUC = 0.810; R = 0.750; Sp = 0.750). IL-1 RN was the only cytokine previously indicated by the univariate data analysis as being significantly higher in more severe patients, despite the low significance (\(p\) = 0.050). Indeed, the predictive model based only on IL-1 RN showed poor prediction performance (AUC = 0.605). This result highlights the relevance of considering a cytokine profile and multivariate data analysis to determine the cytokine subsets that can provide the best predictive models for patient disease severity. The fact that, when evaluating isolated cytokines, no significant differences were observed between Groups A and B, but good predictive models were achieved when considering a set of cytokines, was most probably related to the high variability of the population and the complex inflammatory processes involved in heterogenous critically ill patients. The same methodology was implemented for the nine routine blood analyses. Again, it was possible to improve the model's performance; in this case, the AUC increased from 0.396 when considering the nine features to 0.647 when considering the following three features: LDH, RBCs, CRP. Both CRP and RBCs were significantly different between Groups A and B in the univariate analysis (both \(p\) < 0.050), but not LDH. Despite this model improvement when using a smaller group of routine blood analyses, the one based on six cytokines resulted in better performance (AUC of 0.647 for routine blood analyses, versus 0.810 for cytokines), again highlighting the relevance of cytokines in predicting disease severity. The best model based on hybrid data combined the previous best subsets of six cytokines and three routine blood analyses, achieving an AUC of 0.891, with both recall and specificity of 0.850. Therefore, it is possible to conclude that models including cytokines con sistently outperformed those relying solely on subsets of routine blood analyses (\(p=0.007\) when comparing models with routine blood analyses subsets to models including only cytokines, and \(p=0.003\) when comparing to hybrid models) (Table 5). When looking at the t-SNE plots using the feature subsets that led to the best Naive Bayes models, one can observe better cluster formation (Figure 4d-f) in comparison to when all features of each dataset were used (Figure 4a-c). Similarly to the Naive Bayes models, the best results were also observed when using a feature subset from the hybrid model (Figure 4f). These results show that t-SNE could be a good qualitative tool for preliminary evaluations of this type of data.