MOTOR is a self-supervised, time-to-event (TTE) 143M parameter foundation model which is pretrained on timestamped sequences of events in 55 million electronic health records (EHR) comprising 9 billion clinical events.

TTE models are used for estimating the probability distribution of the time until a specific event occurs, which is an important task in medical settings. We evaluate MOTOR's performance on 19 tasks, across 3 patient databases (a private EHR system, MIMIC-IV, and Merative claims data). Task-specific models adapted from MOTOR improve time-dependent C statistics by 4.6% over state-of-the-art, improve label efficiency by up to 95% ,and are more robust to temporal distributional shifts. We also evaluate cross-site portability by adapting our MOTOR foundation model for six prediction tasks on the MIMIC-IV dataset, where it outperforms all baselines.

Steinberg et al, in ICLR 2024 MOTOR: A Time-To-Event Foundation Model For Structured Medical Records.
Open review at https://openreview.net/forum?id=NialiwI2V6
Model at https://huggingface.co/StanfordShahLab/motor-t-base