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Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach

This study aimed to distinguish hyperkalemia from a normal level based on ECG features. Patients with at least 8 records of hyperkalemia and normokalemia each were adopted for personalized transfer learning. The others were selected for generic model training. ECG excerpts from 10 minutes before the time of serum potassium tests were annotated as corresponding to hyperkalemia or normokalemia according to the test results.

I-Min Chiu, Jhu-Yin Cheng, Tien-Yu Chen, Yi-Min Wang, Chi-Yung Cheng, Chia-Te Kung, Fu-Jen Cheng, Fei-Fei Flora Yau, Chun-Hung Richard Lin

J Med Internet Res 2022;24(12):e41163

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Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study

Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study

Hyperkalemia is a potential life-threatening condition for the general population, and so it can be a clinical and economic burden [1]. Normal levels of potassium are between 3.5 and 5.0 mmol/L with levels above 5.5 mmol/L defined as hyperkalemia. Patients with chronic kidney disease (CKD) are predisposed to hyperkalemia [2], and it is a major risk factor for cardiac arrhythmias and death [3].

Erdenebayar Urtnasan, Jung Hun Lee, Byungjin Moon, Hee Young Lee, Kyuhee Lee, Hyun Youk

JMIR Med Inform 2022;10(6):e34724

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Article has an altmetric score of 2