Predicting antibiotic exposure in IC patients

Master thesis (2022)

Authors

R.N. Gangapersad Electrical Engineering, Mathematics and Computer Science

Contributors

N. Parolya Statistics - EEMCS (supervisor 1)
N.V. Budko Numerical Analysis - EEMCS (coach)
Tim Ewoldt Erasmus MC (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Ravish Gangapersad
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Published Date

14-07-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In this paper, we want to create a prediction model for target attainment and a scoring system based on these models. The Random Forest model, Logistic Regression model, and Naive Bayes model are employed to achieve this. Classification trees and predictors from the random forest model are used to create the scoring system. The theory behind each model and scoring system is described in detail. The properties of proper scoring rules were checked when making our scoring systems. We have made three models which can reasonably predict target attainment. The logistic regression model performed the best for internal and external validation, with an AUC of 0.84 and 0.80, respectively, whereas the RF model performed slightly worse. The AUC of the random forest differed by 0.01 in both validation sets. The scoring system performed reasonably with an AUC of 0.74. These models and the scoring system perform reasonably well and will help the medical practitioner assess if an ICU patient will attain their beta-lactam antibiotic target.