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motor [2024/02/07 18:09]
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motor [2024/03/05 17:37] (current)
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| 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. | 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. |
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| Steinberg et al, in ICLR 2024 [[https://arxiv.org/abs/2301.03150 |MOTOR: A Time-To-Event Foundation Model For Structured Medical Records]]. Open review at https://openreview.net/forum?id=NialiwI2V6 | Steinberg et al, in ICLR 2024 [[https://arxiv.org/abs/2301.03150 |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 |
