Process Mining Model to Predict Mortality in Paralytic Ileus Patients
Published in 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021
Recommended citation: J. Theis and H. Darabi, "Masking Neural Networks Using Reachability Graphs to Predict Process Events," 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021, pp. 1-6, doi: 10.1109/ICCSI53130.2021.9736237. https://ieeexplore.ieee.org/document/9736217
Paralytic Ileus (PI) patients are at high risk of death when admitted to the Intensive care unit (ICU), with mortality as high as 40%. There is minimal research concerning PI patient mortality prediction. There is a need for more accurate prediction modeling for ICU patients diagnosed with PI. This paper demonstrates performance improvements in predicting the mortality of ICU patients diagnosed with PI after 24 hours of being admitted. The proposed framework, PMPI(Process Mining Model to predict mortality of PI patients), is a modification of the work used for prediction of in-hospital mortality for ICU patients with diabetes. PMPI demonstrates similar if not better performance with an Area under the ROC Curve (AUC) score of 0.82 compared to the best results of the existing literature. PMPI uses patient medical history, the time related to the events, and demographic information for prediction. The PMPI prediction framework has the potential to help medical teams in making better decisions for treatment and care for ICU patients with PI to increase their life expectancy.
Recommended citation: M. Pishgar, M. Razo, J. Theis and H. Darabi, "Process Mining Model to Predict Mortality in Paralytic Ileus Patients," 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021, pp. 1-6, doi: 10.1109/ICCSI53130.2021.9736217.