Neural Granger Causal Discovery for Derangements in ICU-Acquired Acute Kidney Injury Patients.

Nowadays, healthcare systems increasingly utilize automated surveillance of electronic medical record (EMR) data to detect adverse events with specific patterns. Despite these technological advances, the early identification of adverse events remains challenging due to the absence of clearly defined prodromal sequences that could signal the onset of such events. Achieving clinically meaningful and interpretable prediction outcomes necessitates a framework that is capable of (i) deducing temporal relationships among various time series features within EMR data (e.g., laboratory test results, vital signs), and (ii) identifying specific patterns that herald the occurrence of an adverse event (e.g., acute kidney injury (AKI)). This study employs a time series forecasting approach to undertake neural Granger causal analysis, and further enhance it by integrating a personalized PageRank algorithm to analyze the critical causal derangements among ICU-acquired AKIpatients. An experimental analysis based on the proposed methodology was conducted using a dataset from MIMIC-IV.Finally, a Granger causality (GC) graph, which revealed several interpretable GC chains that could be used to predict the occurrence ofAKI in ICU settings, was generated. The GC graph and GC chains identified in this study have the potential to aid ICU physicians in providing timely interventions and may help improve patient outcomes.