Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention
Degan Hao Mohammadreza Negahdar
Long COVID is a general term of post-acute sequelae of COVID-19. Patients
with long COVID can endure long-lasting symptoms including fatigue, headache,
dyspnea and anosmia, etc. Identifying the cohorts with severe long-term
complications in COVID-19 could benefit the treatment planning and resource
arrangement. However, due to the heterogeneous phenotype presented in long
COVID patients, it is difficult to predict their outcomes from their
longitudinal data. In this study, we proposed a spatiotemporal attention
mechanism to weigh feature importance jointly from the temporal dimension and
feature space. Considering that medical examinations can have interchangeable
orders in adjacent time points, we restricted the learning of short-term
dependency with a Local-LSTM and the learning of long-term dependency with the
joint spatiotemporal attention. We also compared the proposed method with
several state-of-the-art methods and a method in clinical practice. The methods
are evaluated on a hard-to-acquire clinical dataset of patients with long
COVID. Experimental results show the Local-LSTM with joint spatiotemporal
attention outperformed related methods in outcome prediction. The proposed
method provides a clinical tool for the severity assessment of long COVID.