Traditional Medicine and Ethnomedicine World Conference 2026

Abstract

The clinical course of COVID 19, as well as the immunological reaction, is notable for its extreme variability. Identifying the main associated factors might help understand the disease progression and physiological status of COVID 19 patients. The dynamic changes of the antibody against Spike protein are crucial for understanding the immune response. This work explores a temporal attention (TA) mechanism of deep learning to predict COVID 19 disease severity, clinical outcomes, and Spike antibody levels by screening serological indicators over time. We use feature selection techniques to filter feature subsets that are highly correlated with the target. The specific deep Long Short Term Memory (LSTM) models are employed to capture the dynamic changes of disease severity, clinical outcome, and Spike antibody level. We also propose deep LSTMs with a TA mechanism to emphasize the later blood test records because later records often attract more attention from doctors. Risk factors highly correlated with COVID 19 are revealed. LSTM achieves the highest classification accuracy for disease severity prediction. Temporal Attention Long Short  Term Memory (TA LSTM) achieves the best performance for clinical outcome prediction. For Spike antibody level prediction, LSTM achieves the best performance. The experimental results demonstrate the effectiveness of the proposed models. Simple factors like LDH, Mono ALB, LYMPH DM, and Sex are critical factors in disease severity. LDH, Neu hs CRP, PLT, and Urea are critical factors in clinical outcomes. We further find that Age, RDW CV, PLT, LDH, eGFR (CKD EPI), LYMPH ,RDW SD, PCT, and TCHO are the Top-9 significant predictors of the Spike antibody level. The proposed models can provide a computer aided medical diagnostics system by simply using a time series of serological indicators.