Process Mining Model to Predict Mortality in Paralytic Ileus Patients

TL;DR

This paper introduces PMPI, a process mining-based model that predicts mortality in ICU patients with Paralytic Ileus, achieving high accuracy and aiding clinical decision-making.

Contribution

The study develops a novel process mining framework tailored for PI patient mortality prediction, improving accuracy over existing models.

Findings

PMPI achieves an AUC of 0.82 in mortality prediction.

Incorporates medical history, event timing, and demographics.

Potential to enhance clinical decision-making for ICU patients.

Abstract

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.