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Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study
JMIR Med Inform 2024;12:e48273
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The original paper listed the authors and affiliations as follows:
Stephen Kellett1, BSc, MSc, D Clin; Katherine Easton2, BSc, MRes, Ph D; Martin Cooper3, BA, MSc; Abigail Millings2, BSc, Ph D; Melanie Simmonds-Buckley2, BSc, Ph D; Glenys Parry2, BA, Ph D
1 Sheffield Health & Social Care NHS Foundation Trust, Clinical Psychology Unit, University of Sheffield, Sheffield, United Kingdom
2 Catalyse, UK, Sheffield, United Kingdom
3 Software Engineering, Graphics and Multimedia; Sheffield Hallam University, Sheffield, United
JMIR Ment Health 2021;8(1):e27159
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Disaster eHealth: Scoping Review
Such a framework for technology integration was proposed by Madanian and Parry [24] and recently been applied to prevent the spread of COVID-19, as suggested by Adly et al [25]. In addition, as assessing a variety of national and local medical information is a disaster mitigation requirement [26], it could be facilitated by using big data and cloud computing. Some of these technologies could offer integration within and across disaster phases.
J Med Internet Res 2020;22(10):e18310
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