  Question: Can an encoder-decoder architecture pretrained on a large dataset
of longitudinal electronic health records improves patient outcome predictions?
Findings: In this prognostic study of 6.8 million patients, our denoising
sequence-to-sequence prediction model of multiple outcomes outperformed
state-of-the-art models scuh pretrained BERT on a broad range of patient
outcomes, including intentional self-harm and pancreatic cancer. Meaning: Deep
bidirectional and autoregressive representation improves patient outcome
prediction.
