This notice describes a correction to the above mentioned paper.
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Correction: Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.
J Med Internet Res
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Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
Article Synopsis
- The article DOI: 10.2196/65994 has been corrected to address inaccuracies or errors found in the original publication.
- The correction ensures that the information presented is accurate and reliable for readers and researchers.
- This update is important for maintaining the integrity of scholarly communication in the field.
Improvement of persistent impairments in executive functions and attention following electroconvulsive therapy in a case control longitudinal follow up study.
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View Article and Find Full Text PDFComprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.
J Med Internet Res
November 2024
Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
Background: Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnout, yet research on the introduction of digital technologies in this field remains limited. The combination of continuous and objective wearable sensor data acquired from patients with deep learning techniques holds the potential to overcome the limitations of traditional psychiatric assessments and support clinical decision-making.
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