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

Journal: International journal of medical informatics
Published Date: Nov 17, 2024

Abstract

BACKGROUND: Anomalies in healthcare refer to deviation from the norm of unusual or unexpected patterns or activities related to patients, diseases or medical centres. Detecting these anomalies is crucial for timely interventions and efficient decision-making, helping to identify issues like operational inefficiencies, fraud and emerging health complications.

Authors

  • Farbod Khanizadeh
    Operation & Information Management, Aston Business School, Birmingham. Electronic address: khanizaf@aston.ac.uk.
  • Alireza Ettefaghian
    Anglia Ruskin University, Cambridge. Electronic address: ae36@aru.ac.uk.
  • George Wilson
    School of Computing and Information Science, Anglia Ruskin University, Cambridge. Electronic address: George.Wilson@aru.ac.uk.
  • Amirali Shirazibeheshti
    AT Medics, London. Electronic address: a.shirazibeheshti@nhs.net.
  • Tarek Radwan
    AT Medics, London. Electronic address: tradwan@nhs.net.
  • Cristina Luca
    School of Computing and Information Science, Anglia Ruskin University, Cambridge CB1 1PT, United Kingdom. Electronic address: cristina.luca@aru.ac.uk.

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