Development of deep learning algorithm for detecting dyskalemia based on electrocardiogram

Dyskalemia is a common electrolyte abnormality. Since dyskalemia can cause fatal arrhythmias and cardiac arrest in severe cases, it is crucial to monitor serum potassium (K + ) levels on time. We developed deep learning models to detect hyperkalemia (K + ≥ 5.5 mEq/L) and hypokalemia (K +

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Veröffentlicht in:	Scientific reports 2024-10, Vol.14 (1), p.22868-9, Article 22868
Hauptverfasser:	An, Jung Nam, Park, Minje, Joo, Sunghoon, Chang, Mineok, Kim, Do Hyoung, Shin, Dong Geum, Na, Yeongyeon, Kim, Jwa-Kyung, Lee, Hyung-Seok, Song, Young Rim, Lee, Yeha, Kim, Sung Gyun
Format:	Artikel
Sprache:	eng
Schlagworte:	692/4019/592/75/29 692/699/1585/1 Adult Aged Algorithms Arrhythmia Deep Learning EKG Electrocardiography Female Humanities and Social Sciences Humans Hyperkalemia Hyperkalemia - blood Hyperkalemia - diagnosis Hypokalemia Hypokalemia - blood Hypokalemia - diagnosis Male Middle Aged multidisciplinary Potassium - blood Retrospective Studies Science Science (multidisciplinary) Survival
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Beschreibung
Zusammenfassung:	Dyskalemia is a common electrolyte abnormality. Since dyskalemia can cause fatal arrhythmias and cardiac arrest in severe cases, it is crucial to monitor serum potassium (K + ) levels on time. We developed deep learning models to detect hyperkalemia (K + ≥ 5.5 mEq/L) and hypokalemia (K +
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-024-71562-5