English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background  Acute Kidney Injury (AKI) is a shared complication among Intensive Care Unit (ICU), marked by high cost, high morbidity and high mortality. As the early prediction of AKI is critical for patients’ outcomes and data mining is such a powerful prediction tool, many AKI prediction models based on machine learning methods have been proposed. Our motivation is inspired by the fact that the incidence of AKI is a changing temporal sequence affected by the joint action of patients’ daily drug combinations and their physiological indexes. However, most existing models have not considered such a temporal correlation. Besides, due to great challenges caused by sparse, high-dimensional and highly imbalanced clinical data, it is hard to achieve ideal performance.    Methods  We develop a fast, simple and less-costly model based on an ensemble learning algorithm, named Ensemble Time Series Model (ETSM). Besides benefiting from vital signs and laboratory results as explicit indicators, ETSM explores the effect of drug combinations as possible implicit indicators for the AKI prediction. The model transforms temporal medication information into a multidimensional vector to consider and measure drug cumulative effects that may cause AKI.    Results  We compare ETSM with state-of-the-art models on ICUC and MIMIC III datasets. On the basis of the experimental results, our model obtains satisfactory performance (ICUC: AUC 24 hours ahead: 0.81, 48 hours ahead: 0.78; MIMIC III: AUC 24 hours ahead: 0.95, 48 hours ahead: 0.95). Meanwhile, we compare the effects of different sampling and feature generation methods on the model performance. In the ablation study, we validate that medication information improves model performance (24 hours ahead: AUC increased from 0.74 to 0.81). We also find that the model’s performance is closely related to the balanced level of the derivation dataset. The optimal ratio of major class size to minor class size for the model is found for AKI prediction.    Conclusions  ETSM is an effective method for the early prediction of AKI. The model verifies that AKI incidence is related to the clinical medication. In comparison with other prediction methods, ETSM provides comparable performance results and better interpretability.

Utilizing imbalanced electronic health records to predict acute kidney injury by ensemble learning and time series model