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• W3208854813 abstract "Delirium is a common acute onset brain dysfunction in the emergency setting and is associated with higher mortality. It is difficult to detect and monitor since its presentations and risk factors can be different depending on the underlying medical condition of patients. In our study, we aimed to identify subtypes within the delirium population and build subgroup-specific predictive models to detect delirium using Medical Information Mart for Intensive Care IV (MIMIC-IV) data. We showed that clusters exist within the delirium population. Differences in feature importance were also observed for subgroup-specific predictive models. Our work could recalibrate existing delirium prediction models for each delirium subgroup and improve the precision of delirium detection and monitoring for ICU or emergency department patients who had highly heterogeneous medical conditions." @default.
• W3208854813 created "2021-11-08" @default.
• W3208854813 creator A5014355203 @default.
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• W3208854813 date "2021-10-31" @default.
• W3208854813 modified "2023-09-25" @default.
• W3208854813 title "Unsupervised Learning to Subphenotype Delirium Patients from Electronic Health Records" @default.
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• W3208854813 doi "https://doi.org/10.48550/arxiv.2111.00592" @default.
• W3208854813 hasPublicationYear "2021" @default.
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