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Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.
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The subject of this deck is the small Print[A] program in the following blog post by Noel Welsh: https://www.inner-product.com/posts/direct-style-effects/. Keywords: "direct-style", "context function", "context functions", "algebraic effect", "algebraic effects", "scala", "effect system", "effect systems", "effect", "side effect", "composition", "fp", "functional programming"
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Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
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Direct Style Effect Systems -The Print[A] Example- A Comprehension Aid
Direct Style Effect Systems -The Print[A] Example- A Comprehension Aid
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