Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care.

ABSTRACT: We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time

SUBMITTER: Haas O 

PROVIDER: S-EPMC8606583 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature