Predicting COVID-19 and pneumonia complications from admission texts. (arXiv:2305.03661v1 [cs.CL])

In this paper we present a novel approach to risk assessment for patients
hospitalized with pneumonia or COVID-19 based on their admission reports. We
applied a Longformer neural network to admission reports and other textual data
available shortly after admission to compute risk scores for the patients. We
used patient data of multiple European hospitals to demonstrate that our
approach outperforms the Transformer baselines. Our experiments show that the
proposed model generalises across institutions and diagnoses. Also, our method
has …

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