Background Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. Objective This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. Methods This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network–based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. Results The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). Conclusions By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one’s ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.
BACKGROUND Hyperkalemia is a critical condition especially in the intensive care unit. There was no accurate and noninvasive method for recognizing hyperkalemia event from ambulatory electrocardiogram monitor so far. OBJECTIVE This study aims at improving the accuracy on hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors through a personalized transfer learning method by training a generic model and fine tuning it with personal data. METHODS This is a retrospective cohort study using open-source data from Waveform Database Matched Subset from Medical information Mart from Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results, and matched ECG data from MIMIC-III database. A one-dimensional convolution neural network based deep learning model is first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-art performance, it was then utilized in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. RESULTS The results show that by acquiring a few data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly from an average of 0.604 ± 0.211 to 0.980 ± 0.078 when compared with the generic model. The Area Under the receiver operating characteristic Curve level also improved from 0.729 ± 0.240 to 0.945 ± 0.094. CONCLUSIONS By utilizing deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection from ambulatory ECG monitor. These findings could potentially be extended to applications that continuously monitor one's ECG for early alerts of hyperkalemia and avoiding unnecessary blood tests. CLINICALTRIAL This study were approved by the Institutional Review Board of Chang Gung Medical Foundation (number: 202001217B0, date of approval: 21/07/2020).
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