Name
Title | ||
|---|---|---|
Temporal dynamics of clinical risk predictors for hospital-acquired acute kidney injury under different forecast time windows |
Abstract | ||
|---|---|---|
Massive electronic medical records provide unique opportunities and possibilities for gaining medical knowledge and actionable insights to transform healthcare. Acute kidney injury (AKI) is considered one of the most common complications of acute illness, with a substantial impact on patient outcome and hospital costs. In this study, we developed a machine-learning-based knowledge mining approach, combining eXtreme Gradient Boosting (XGBoost) model and SHapley Additive exPlanations (SHAP) method, to explore the temporal dynamics of clinical risk predictors for hospital-acquired AKI under different forecast time windows. AKI occurred in 7,259 (9.43%) of 76,957 hospital admissions from November 2007 to December 2016 extracted from our institution’s de-identified clinical database. Through qualitative visualization and quantitative analysis, we found and confirmed that relative importance of optimal risk factors fluctuates with the size of AKI prediction time window. For example, the contribution of most time-varying variables (such as medication and lab tests) to AKI risk increases as AKI onset approaches, while most non-time-varying variables (such as age and admission diagnosis) decrease. Besides, the optimal set of AKI risk predictors will also change under different time window. This study provides several practical implications, including recognizing the existence of feature weight volatility as it is desirable for model accuracy, identifying important AKI risk factor for further investigation, and facilitating early accurate prediction of AKI. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.knosys.2022.108655 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Acute kidney injury,Knowledge mining approach,Risk prediction,Temporal fluctuation,Electronic medical records | Journal | 245 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lijuan Wu | 1 | 0 | 0.34 |
| Yong Hu | 2 | 197 | 38.46 |
| Xiangzhou Zhang | 3 | 0 | 0.34 |
| Borong Yuan | 4 | 0 | 0.34 |
| Weiqi Chen | 5 | 0 | 0.34 |
| Kang Liu | 6 | 0 | 0.34 |
| Mei Liu | 7 | 0 | 0.34 |