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November 17, 2024International Journal of Medical Informatics10 citationsOpen Access

Smart data-driven medical decisions through collective and individual anomaly detection in healthcare time series

FKFarbod KhanizadehAEAlireza EttefaghianGWGeorge Wilson

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Abstract

This study underscores the importance of integrating unsupervised anomaly detection with clinical expertise to ensure that statistically anomalous patterns align with clinical relevance. The dual-strategy clustering method holds significant potential for enabling timely interventions, proactively identifying potential crises, and ultimately contributing to better decision-making and operational efficiency within the healthcare sector.

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Khanizadeh et al. (2024) studied this question.

synapsesocial.com/papers/69d8407b5c3030ff03d1998fhttps://doi.org/10.1016/j.ijmedinf.2024.105696
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