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femr [2023/12/13 10:44] nigam created |
femr [2023/12/13 10:44] (current) nigam |
Framework for Electronic Medical Records (FEMR) | ====== Framework for Electronic Medical Records (FEMR) ====== |
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FEMR is a Python package for manipulating longitudinal EHR data for machine learning, with a focus on supporting the creation of foundation models and verifying their [[https://hai.stanford.edu/news/how-foundation-models-can-advance-ai-healthcare | presumed benefits]] in healthcare. Such a framework is needed given the [[https://hai.stanford.edu/news/shaky-foundations-foundation-models-healthcare | current state of large language models in healthcare]] and the need for better evaluation frameworks. | FEMR is a Python package for manipulating longitudinal EHR data for machine learning, with a focus on supporting the creation of foundation models and verifying their [[https://hai.stanford.edu/news/how-foundation-models-can-advance-ai-healthcare | presumed benefits]] in healthcare. Such a framework is needed given the [[https://hai.stanford.edu/news/shaky-foundations-foundation-models-healthcare | current state of large language models in healthcare]] and the need for better evaluation frameworks. |
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FEMR by default supports the [[ https://www.ohdsi.org/data-standardization/ | OMOP Common Data Model]] developed by the OHDSI community, but can also be used with other forms of EHR / claims data with minimal processing. FEMR has been used to process data from a variety of sources, including MIMIC-IV, Optum, Truven, STARR-OMOP, and SickKids-OMOP. | FEMR by default supports the [[ https://www.ohdsi.org/data-standardization/ | OMOP Common Data Model]] developed by the OHDSI community, but can also be used with other forms of EHR / claims data with minimal processing. FEMR has been used to process data from a variety of sources, including MIMIC-IV, Optum, Truven, STARR-OMOP, and SickKids-OMOP. |
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| For details, see the github repo -- https://github.com/som-shahlab/femr |