  Due to the characteristics of COVID-19, the epidemic develops rapidly and
overwhelms health service systems worldwide. Many patients suffer from systemic
life-threatening problems and need to be carefully monitored in ICUs. Thus the
intelligent prognosis is in an urgent need to assist physicians to take an
early intervention, prevent the adverse outcome, and optimize the medical
resource allocation. However, in the early stage of the epidemic outbreak, the
data available for analysis is limited due to the lack of effective diagnostic
mechanisms, rarity of the cases, and privacy concerns. In this paper, we
propose a deep-learning-based approach, CovidCare, which leverages the existing
electronic medical records to enhance the prognosis for inpatients with
emerging infectious diseases. It learns to embed the COVID-19-related medical
features based on massive existing EMR data via transfer learning. The
transferred parameters are further trained to imitate the teacher model's
representation behavior based on knowledge distillation, which embeds the
health status more comprehensively in the source dataset. We conduct the length
of stay prediction experiments for patients on a real-world COVID-19 dataset.
The experiment results indicate that our proposed model consistently
outperforms the comparative baseline methods. CovidCare also reveals that, 1)
hs-cTnI, hs-CRP and Platelet Counts are the most fatal biomarkers, whose
abnormal values usually indicate emergency adverse outcome. 2) Normal values of
gamma-GT, AP and eGFR indicate the overall improvement of health. The medical
findings extracted by CovidCare are empirically confirmed by human experts and
medical literatures.
