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  # Dataset Card: SIMORD (Simulated Medical Order Extraction Dataset)
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  Medical order extraction involves identifying and structuring various medical orders —such as medications, imaging studies, lab tests, and follow-ups— based on doctor-patient conversations. Previous efforts have focused on extracting entities and relations from clinical texts.
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- This shared task seeks to develop effective solutions for improving clinical documentation, reducing the burden on providers, and ensuring critical patient information is accurately captured from long conversations.
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  The input dialogues are sourced from a combination of existing conversational datasets (e.g., ACI-Bench [1], PriMock57 [2]), and structured lists of medical orders are created by medical annotators.
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  # Dataset Card: SIMORD (Simulated Medical Order Extraction Dataset)
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+ ## Description
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  Medical order extraction involves identifying and structuring various medical orders —such as medications, imaging studies, lab tests, and follow-ups— based on doctor-patient conversations. Previous efforts have focused on extracting entities and relations from clinical texts.
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+ This dataset seeks to encourage the developement of effective solutions for improving clinical documentation, reducing the burden on providers, and ensuring critical patient information is accurately captured from long conversations.
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  The input dialogues are sourced from a combination of existing conversational datasets (e.g., ACI-Bench [1], PriMock57 [2]), and structured lists of medical orders are created by medical annotators.
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