Browsing by Author "Murat, B."

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Detection of Hypokalemia, Hyponatremia, and Hyperkalemia in Heart Failure Patients Using Artificial Intelligence Techniques via Electrocardiography
(2026) Iyigun, U.; Kerkutluoglu, M.; Güneş, H.; Kahramanoğullari, Faris; Kivrak, T.; Murat, B.; Küçükler, N.
OBJECTIVE: Detection and monitoring of electrolyte imbalances are essential for the appropriate treatment of many metabolic diseases. However, no reliable and noninvasive tool currently exists for such detection. Electrolyte disorders, particularly in heart failure patients, can lead to life-threatening situations, which may often develop as a result of medications used in routine treatment. METHOD: In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) to detect electrolyte imbalances in heart failure patients and evaluated its performance in a multicenter setting. Seventeen different centers participated in this study. Heart failure patients (ejection fraction ≤ 45%) who had blood electrolyte measurements and ECG taken on the same day were included. Patients were divided into four groups: those with normal electrolyte values, those with hypokalemia, those with hyperkalemia, and those with hyponatremia. Patients who developed electrolyte disorders due to medications used for heart failure were classified in the relevant group. Confidence intervals (CI): We computed 95% CIs for area under the receiver operating characteristic curve (AUROC) via stratified bootstrap (2,000 resamples at the patient level) and 95% CIs for accuracy using the Wilson score interval for binomial proportions. RESULTS: The accuracy rates of the DLM in detecting hyponatremia, hypokalemia, and hyperkalemia were 83.33%, 95.33%, and 95.77%, respectively. CONCLUSION: The proposed DLM demonstrated high performance in detecting electrolyte imbalances. These results suggest that a DLM can be used to detect and monitor electrolyte imbalances using ECG on a daily basis.
A Symptom Evaluation System on Medical Diagnosis
(Institute of Electrical and Electronics Engineers Inc., 2023) Murat, B.; Uzer, A.O.; Ketenci, S.; Yasbek, S.; Korkmaz, I.
The Symptom Evaluation System (SES) to be used on medical diagnosis is a software application that offers time-saving solutions to the problems regarding the communication and the relationship between patients and doctors. The motivation to propose such an application is to focus on and attempt to solve the problem of time loss experienced by patients and doctors and so to prevent starting the treatment process later than possible. SES, in terms of doctors, aims to solve the problems caused by the patients' inability to express themselves correctly during the examination and the extra time loss problem experienced during the examination process. SES has been developed using Flutter and Go to offer both web and mobile applications. SES includes an appointment section where patients get an appointment from their doctors if available. The appointment section also allows patients to enter their symptoms with severity level, comments and extra information. These features help doctors save time by dissecting information about the patient before the face-to-face examination. © 2023 IEEE.