Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients
| Autor: | Chen-Xi Wang, Yi-Chu Zhang, Qi-Lin Kong, Zu-Xiang Wu, Ping-Ping Yang, Cai-Hua Zhu, Shou-Lin Chen, Tao Wu, Qing-Hua Wu, Qi Chen, Peng Lyu |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Chinese Medical Journal, Vol 134, Iss 19, Pp 2333-2339 (2021) |
| Druh dokumentu: | article |
| ISSN: | 0366-6999 2542-5641 00000000 |
| DOI: | 10.1097/CM9.0000000000001650 |
| Popis: | Abstract. Background:. A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients. Methods:. We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1–6) to detect patients with serum potassium concentrations |
| Databáze: | Directory of Open Access Journals |
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