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@@ -40,4 +40,16 @@ The patient notes and questions come from the following four datasets:
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  We hope this data set of patient summaries and medical examination questions can be helpful for researchers looking to benchmark the performance
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  of large language models (LLMs) on medical entity extraction and also benchmark LLM's performance in using these extracted entitites
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- to perform different medical calculations.
 
 
 
 
 
 
 
 
 
 
 
 
 
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  We hope this data set of patient summaries and medical examination questions can be helpful for researchers looking to benchmark the performance
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  of large language models (LLMs) on medical entity extraction and also benchmark LLM's performance in using these extracted entitites
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+ to perform different medical calculations.
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+
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+ If you find this dataset useful, please cite our paper by:
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+ ```bibtex
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+ @article{khandekar2024medcalc,
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+ title={Medcalc-bench: Evaluating large language models for medical calculations},
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+ author={Khandekar, Nikhil and Jin, Qiao and Xiong, Guangzhi and Dunn, Soren and Applebaum, Serina and Anwar, Zain and Sarfo-Gyamfi, Maame and Safranek, Conrad and Anwar, Abid and Zhang, Andrew and others},
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+ journal={Advances in Neural Information Processing Systems},
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+ volume={37},
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+ pages={84730--84745},
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+ year={2024}
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+ }
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+ ```