Transformer-based Time-to-Event Prediction for Chronic Kidney Disease
  Deterioration
Moshe Zisser Dvir Aran
Deep-learning techniques, particularly the transformer model, have shown
great potential in enhancing the prediction performance of longitudinal health
records. While previous methods have mainly focused on fixed-time risk
prediction, time-to-event prediction (also known as survival analysis) is often
more appropriate for clinical scenarios. Here, we present a novel deep-learning
architecture we named STRAFE, a generalizable survival analysis
transformer-based architecture for electronic health records. The performance
of STRAFE was evaluated using a real-world claim dataset of over 130,000
individuals with stage 3 chronic kidney disease (CKD) and was found to
outperform other time-to-event prediction algorithms in predicting the exact
time of deterioration to stage 5. Additionally, STRAFE was found to outperform
binary outcome algorithms in predicting fixed-time risk, possibly due to its
ability to train on censored data. We show that STRAFE predictions can improve
the positive predictive value of high-risk patients by 3-fold, demonstrating
possible usage to improve targeting for intervention programs. Finally, we
suggest a novel visualization approach to predictions on a per-patient basis.
In conclusion, STRAFE is a cutting-edge time-to-event prediction algorithm that
has the potential to enhance risk predictions in large claims datasets.