  The coronavirus disease 2019 (COVID-19) has led to a global pandemic of
significant severity. In addition to its high level of contagiousness, COVID-19
can have a heterogeneous clinical course, ranging from asymptomatic carriers to
severe and potentially life-threatening health complications. Many patients
have to revisit the emergency room (ER) within a short time after discharge,
which significantly increases the workload for medical staff. Early
identification of such patients is crucial for helping physicians focus on
treating life-threatening cases. In this study, we obtained Electronic Health
Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University
of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged
a Natural Language Processing technique, ScispaCy, to extract clinical concepts
and used the 1001 most frequent concepts to develop 7-day revisit models for
COVID-19 patients in ERs. The research data we collected from 13 ERs may have
distributional differences that could affect the model development. To address
this issue, we employed a classic deep transfer learning method called the
Domain Adversarial Neural Network (DANN) and evaluated different modeling
strategies, including the Multi-DANN algorithm, the Single-DANN algorithm, and
three baseline methods. Results showed that the Multi-DANN models outperformed
the Single-DANN models and baseline models in predicting revisits of COVID-19
patients to the ER within 7 days after discharge. Notably, the Multi-DANN
strategy effectively addressed the heterogeneity among multiple source domains
and improved the adaptation of source data to the target domain. Moreover, the
high performance of Multi-DANN models indicates that EHRs are informative for
developing a prediction model to identify COVID-19 patients who are very likely
to revisit an ER within 7 days after discharge.
