Prediction of hospital mortality in intensive care unit patients from clinical and laboratory data: A machine learning approach

Figure 1 Methodological design of the study. The proposed workflow encompasses selective collection of clinical, laboratorial and outcome data, splitting and pre-processing of the data, iterative training of the classificatory model, and finally evaluation of its performance. ICU: Intensive care unit.

Figure 2 Model confusion matrix. As illustrated, the model was able to accurately predict occurrence of death outcome for 63 of 77 patients and non-occurrence for 112 of 131 patients, with true positive and true negative rates of 76.8% and 88.9%, respectively.

Figure 3 Model receiver operating characteristic curve. The graph demonstrates the relationship between true and false positive rates, which led to an area under the curve of 85%. AUC: Area under the curve.