Vinod Kumar Chauhan, Anshul Thakur, Odhran O'Donoghue, David A. Clifton
TL;DR该研究旨在应对电子健康记录中不规则时间序列带来的机器学习算法训练挑战,提出了一种使用神经常微分方程的COntinuous patient state PERceiver模型,被证明在MIMIC-III数据集中具有较好的预测效果。
Abstract
In electronic health records (EHRs), irregular time-series (ITS) occur naturally due to patient health dynamics, reflected by irregular hospital visits, diseases/conditions and the necessity to measure different