Predicting COVID-19 and pneumonia complications from admission texts
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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Methods
Absolute Position Encodings •
Adam •
AdamW •
Attention Dropout •
BPE •
Dense Connections •
Dilated Sliding Window Attention •
Dropout •
GELU •
Global and Sliding Window Attention •
Label Smoothing •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Longformer •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Sliding Window Attention •
Softmax •
Transformer •
Weight Decay •
WordPiece
