Predicting COVID-19 and pneumonia complications from admission texts

• URL: http://arxiv.org/abs/2305.03661v1
• Date: Fri, 5 May 2023 16:28:44 GMT
• Title: Predicting COVID-19 and pneumonia complications from admission texts
• Authors: Dmitriy Umerenkov, Oleg Cherkashin, Alexander Nesterov, Victor Gombolevskiy, Irina Demko, Alexander Yalunin, Vladimir Kokh
• Abstract summary: 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.
• Score: 101.28793285063904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 several other advantages described in the paper.

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