Predicting antibiotic exposure in IC patients

Predicting antibiotic exposure in IC patients

Gangapersad, Ravish (TU Delft Electrical Engineering, Mathematics and Computer Science)

Parolya, N. (mentor)
Budko, N.V. (graduation committee)
Ewoldt, Tim (graduation committee)

Delft University of Technology

2022-07-14

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.

Machine learning
Hospital data
random forest
Antibiotics
Scoring system

http://resolver.tudelft.nl/uuid:87f1128a-14e4-48a9-95df-f73ed7bf91ce

Student theses

master thesis

© 2022 Ravish Gangapersad