# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
## 4 Discussion
Moreover, random forests are more easily scalable than standard statistical techniques, as they do not rely on assumptions of linearity between the log-odds of the outcomes and independent predictors, independency, and multicollinearity, challenges that are common in EHR data, and which are used for standard logistic regression techniques [33]. Random forests may further prove useful in clinical setting, as they may be used in the pre-processing phase for imputation of missing data. We adopted this method in the present study, and it again outperformed standard imputation techniques. Moreover, in clinical situations, random forests retain interpretability, an advantage over more complicated machine learning approaches such as unsupervised or deep learning. On this point, a growing body of research emphasizes the significance of explainable machine learning algorithms applied to the prediction of clinical outcomes [34]. [PERSON] et al. provide a two local-level explanation technique to aid clinicians in the understanding of model prediction for a specific patient with sepsis [35]. Other methods, such as the use of heatmaps to allow interpretability of deep-learning models by highlighting the most influential variables in temporal sequences, have also been proposed [36]. Mortality in patients arriving at the ED from home was significantly lower in univariate analysis than mortality in long-term care patients (10% vs. 48%, \(p<\) 0.001). As most of our patients (about 3 out of 4 patients) arrived at the ED from home, they were exposed to common community-acquired pathogens with low antibiotic resistance, compared to frail populations. This, in turn, implied that the ED physician could apply guidelines for empiric antibiotic treatment of community-acquired infections with satisfactory outcomes [37]. Age was significantly associated with mortality in both univariate analysis and machine learning models. Extreme age is a very well-known factor influencing survival in the ED population. The median age in our population is higher than that in previous studies, while mortality is comparable with that of the population [38; 39]. [PERSON] et al., for example, reported a population of ED patients with sepsis with a mean age of 69 and mortality of 23%, compared to the median age of 77 years (IQR 69-83) in our cohort, which reached 15% mortality [38]. Both hyponatremia and hypernatremia have been recognized as independent factors for mortality in ED and critically ill patients [40; 41]. In our results, the random forest model and the balanced and unbalanced logistic regression identified levels of natremia as an independent factor for mortality, confirming previous findings. High heart rate (HR) and elevated C-reactive protein (CPR) levels were also found to be associated with increased mortality, consistent with previous literature. [PERSON] et al., for example, investigated whether heart rate variability (HRV) was a predictor of mortality in septic patients. They found that several HRV parameters were reduced in non-surviving septic patients [16]. [PERSON] et al., in the same fashion, found that the risk of sepsis-related mortality appears to be increased when the third-day CRP value is greater than 100 mg/dL [17]. A further interesting correlation emerged between blood glucose levels and mortality during sepsis, reflecting the most current literature data. In a retrospective sub-analysis of a multicenter, prospective cohort study, [PERSON] and colleagues investigated the impact of blood glucose abnormalities on outcome and disease severity in patients with severe sepsis. They found that hypoglycemia was related to increased severity and high mortality in septic patients [42]. Gender did not influence survival in this population. Studies on hospitalized patients suggest gender influences mortality, and consider variations in the inflammatory response according to patient gender.