Learning Physiological Mechanisms that Predict Adverse Cardiovascular Events in Intensive Care Patients with Chronic...

Chronic heart disease is a burdensome, complex, and fatal condition. Learning the mechanisms driving the development of heart disease is key to early risk assessment and intervention. However, many current machine learning approaches lack sufficient interpretability. Using 2,737 patients with chronic heart disease from the MIMIC-III database, we trained an interpretable Tropical Geometry Fuzzy Neural Network to predict one-year occurrence of a severe cardiac procedure or mortality (AUROC=0.663). We present the 20 learned rules which explain the model predictions. We find that the rules are clinically valid and indicate underlying pathologies. We anticipate that with additional development and validation, these rules will aid clinicians in providing preventative care for chronic heart disease patients in intensive care units.