Work

Machine Learning for Risk Prediction of Recurrent AKI in Adult Patients After Hospital Discharge

Year: 2024

Type: book-chapter

Abstract: Recurrent AKI has been found common among hospitalized patients after discharge, and early prediction may allow timely intervention and optimized post-discharge treatment [1]. There are significant gaps in the literature regarding the risk prediction on the post-AKI population, and most current works only included a limited number of pre-selected variables [2]. In this study, we built and compared machine learning models using both knowledge-based and data-driven features in predicting the risk of recurrent AKI within 1-year of discharge. Our results showed that the additional use of data-driven features statistically improved the model performances, with best AUC=0.766 by using logistic regression.

Source: Studies in health technology and informatics

DOI: 10.3233/shti230959

Authors: Jianqiu Zhang, Paul E. Drawz, Gyorgy Simon, Terrence J. Adam, Genevieve B. Melton

Institutions: University of Minnesota, Fairview Health Services

Language: English

Topic: Acute Kidney Injury Research

Subfield: Nephrology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Institutes of Health, Agency for Healthcare Research and Quality