Abstract
External validation of models for the prediction of acute kidney injury (AKI) is rare. We externally validate AKI prediction models in intensive care units. The models were developed on the Medical Information Mart for Intensive Care dataset and validated on the eICU dataset. Traditional machine learning models show limited transportability to the new population (AUROC < 0.8). Models based on recurrent neural networks, which can capture complex relationships between the data, transport well to the new population (AUROC 0.8-0.9). Such models can help clinicians to recognize AKI and improve the outcome.
Original language | English |
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Pages (from-to) | 148-151 |
Number of pages | 4 |
Journal | Studies in health technology and informatics |
Volume | 295 |
DOIs | |
Publication status | Published - 29 Jun 2022 |
Keywords
- Acute kidney injury
- ICU
- clinical prediction models
- external validation
- machine learning