Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database

Abstract

Acute kidney injury (AKI) is a common complication in critically ill patients and is associated with long-term complications and an increased mortality. This work presents preliminary findings from the first freely available European intensive care database released by Amsterdam UMC. A machine learning (ML) model was developed to predict AKI in the intensive care unit 12 hours before the actual event. Main features of the model included medications and hemodynamic parameters. Our models perform with an accuracy of 81.8% on moderate to severe AKI and 79.8% on all AKI patients. Those results can compete with models reported in the literature and introduce an ML model for AKI based on European patient data.

Machine learning for acute kidney injury: Changing the traditional disease prediction mode

“…Deep learning is the process of learning the intrinsic laws and levels of the representation of sample data, and the information obtained from these learning processes can be of great help in the interpretation of data such as text, images and sound. As the complexity of graph models in deep learning leads to a dramatic increase in the time complexity of the algorithm, higher parallel programming skills and more and better hardware support are needed to ensure the real-time performance of the algorithm available databases, MIMIC-III, AmsterdamUMCdb and eICU (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). The use of local databases is not common, and only information from the Mayo Clinic and SHZJU-ICU can be retrieved (21,26,27).…”

“…In some studies that also incorporated traditional modeling methods, such as logistic regression, or compared with physicians' subjective diagnoses, machine learning modeling was also simultaneously superior. Fourth, interpretability is very important in the medical field, and a medically assisted diagnostic system must be understandable and interpretable; ideally, it should be able to explain the complete logic of providing corresponding decisions to all relevant parties to gain the trust of physicians, but the process of achieving model interpretation in the construction of predictive models regarding AKI for critically ill patients is rare, which somehow has led to a disconnect between modeling and analysis, forgoing additional analysis of AKI characteristics and risk factors and wasting the potential for the effective use of large amounts of information (19,22,26). Fifth, while variable screening for modeling is necessary and critical, the inclusion of static and dynamic variables reflects different modeling objectives.…”