Presentation Information
[SS15-02]Integrating Modeling and AI for Early Prediction of Disease Progression
*Shingo Iwami1 (1. Nagoya University (Japan))
Keywords:
machine learning,mathematical model
Acute liver failure (ALF) is a heterogeneous syndrome with poor prognosis, and the lack of an appropriate classification has been a barrier to understanding the pathophysiology of the disease and to developing treatments. While liver transplantation may be required for some patients with ALF, obstacles such as organ scarcity and age restrictions present significant challenges. Moreover, appropriately selecting patients and determining the timing of surgery has proven daunting, especially considering the substantial variations in disease progression of acute liver injury (ALI) to ALF. One promising strategy for improving ALI prognosis is to predict which ALI patients would advance to ALF and initiate therapeutic interventions early. In this study, to address this issue and enhance treatment outcomes for patients with ALI, we have devised an approach that predicts ALI progression at both group and individual levels during the early phase of ALI.