Date: July 7, 2022

Source: Big Data Decision Institute

Dr. Wu Lijuan, an assistant professor at JNU, has published her research findings as the first author in Knowledge-Based Systems (SCI District 1, Chinese Academy of Sciences, IF=8.139) and International Journal of Medical Informatics (SCI District 2, Chinese Academy of Sciences, IF=4.730).

(Paper's Abstract)

Titled Temporal dynamics of clinical risk predictors for hospital-acquired acute kidney injury under different forecast time windows, Wu's publication in Knowledge-Based Systems proposed a mining method based on machine learning to explore the temporal fluctuation of clinical risk factors for acute kidney injury (AKI) under different predicted time windows. The results confirmed that the relative importance of clinical risk predictors fluctuate with the change of AKI prediction time window and that the subset of optimal prediction characteristics will also vary. The study emphasizes the temporal fluctuation regularity of disease risk predictors, which contributes to early accurate prediction of AKI.

Wu's paper Development of a knowledge mining approach to uncover heterogeneous risk predictors of acute kidney injury across age groups in International Journal of Medical Informatics shows that the knowledge-mining model established by using Electronic Medical Records will make it possible to explore new knowledge of potential risk and enhance personalized interpretation. The study offers the possibility of enhancing disease prevention in clinical care. Such heterogeneity will be needed in future decision support systems to enhance personalized patient care.

The research is supported by the Youth Fund of National Natural Science Foundation of China, the Research and Cultivation Fund of the Central Universities, and the Basic Research Project of Guangzhou.

Link to the paper:

https://www.sciencedirect.com/science/article/abs/pii/S0950705122003008