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foundationmodels [2024/03/07 17:19]
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foundationmodels [2024/03/07 17:20]
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| See the full post at [[https://hai.stanford.edu/news/how-foundation-models-can-advance-ai-healthcare|How Foundation Models Can Advance AI in Healthcare]]. To support the claims made in the post, we have built and released two foundation models: | See the full post at [[https://hai.stanford.edu/news/how-foundation-models-can-advance-ai-healthcare|How Foundation Models Can Advance AI in Healthcare]]. To support the claims made in the post, we have built and released two foundation models: |
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| - [[https://clmbr.stanford.edu/ | CLMBR (clinical language modeling based representations)]] is a 141 million parameter autoregressive foundation model pretrained on 2.57 million deidentified EHRs from Stanford Medicine. This model is based on the CLMBR architecture originally described in [[https://www.sciencedirect.com/science/article/pii/S1532046420302653 |Steinberg et al. 2021]]. As input, this model expects a sequence of coded medical events that have been mapped to Standard Concepts within the OMOP-CDM vocabulary. The model generates representations of patients which can then be used for downstream prediction tasks. | - [[https://clmbr.stanford.edu/ | CLMBR (clinical language modeling based representations)]] is a 141 million parameter autoregressive foundation model pretrained on 2.57 million deidentified EHRs from Stanford Medicine. This model is based on the CLMBR architecture originally described in [[https://www.sciencedirect.com/science/article/pii/S1532046420302653 |Steinberg et al. 2021]]. As input, this model expects a sequence of coded medical events that have been mapped to Standard Concepts within the OMOP-CDM vocabulary. The model generates representations of patients which can then be used for downstream prediction tasks. The model is available at – https://huggingface.co/StanfordShahLab/clmbr-t-base |
| | - [[https://goto.stanford.edu/motor | MOTOR (Many Outcome Time Oriented Representations)]] 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. |
| For details see – https://huggingface.co/StanfordShahLab/clmbr-t-base | |
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