Liefstingh, Menno (2018) Predicting Sepsis-Induced Patient Deterioration Using Machine Learning. Bachelor's Thesis, Artificial Intelligence.
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Abstract
Sepsis is one of the leading causes of in-hospital mortality, and patients benefit greatly from early detection. Using data collected at the emergency room of the University Medical Center of Groningen, this research compares a number of different algorithms and imputation methods to predict multiple kinds of sepsis-induced patient deterioration to see what machine learning could be capable of for risk assessment and early detection. Challenges with this data are the relatively low amount of inclusions and high amount of missing values. Results show that ensemble methods outperform other algorithms on this dataset and that MICE imputation can provide a clear performance boost for those algorithms.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Wiering, M.A. and Quinten, V.M. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 30 Jul 2018 |
| Last Modified: | 31 Jul 2018 12:21 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/18147 |
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