{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:38:33Z","timestamp":1787841513089,"version":"build-2784847793"},"reference-count":19,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"8","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:title>\n                    A\n                    <jats:sc>BSTRACT<\/jats:sc>\n                  <\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/jfmpc.jfmpc_2025_23","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T02:08:46Z","timestamp":1721959726000},"page":"3393-3397","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Rapid and non-invasive diagnosis of hyperkalemia in patients with systolic myocardial failure using a model based on machine learning algorithms"],"prefix":"10.4103","volume":"13","author":[{"given":"Hamid M.","family":"Torshizi","sequence":"first","affiliation":[{"name":"Phd Student of Biomedical Engineering and Physics Department, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad R.","family":"Khorgami","sequence":"additional","affiliation":[{"name":"Rajaie Heart Center and Department of Pediatric Cardiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Negar","family":"Omidi","sequence":"additional","affiliation":[{"name":"Cardiac Primary Prevention Research Center, Cardiovascular Disease Research Institute, Tehran University of Medical Science, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fattaneh","family":"Khalaj","sequence":"additional","affiliation":[{"name":"Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsen","family":"Ahmadi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"R1-20240828","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1161\/CIR.0b013e31820a55f5","article-title":"Forecasting the future of cardiovascular disease in the United States: A policy statement from the American Heart Association","volume":"123","author":"Heidenreich","year":"2011","journal-title":"Circulation"},{"key":"R2-20240828","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1111\/j.1471-6712.2012.01078.x","article-title":"Health-related quality of life of patients with severe heart failure. A cross-sectional multicentre study","volume":"27","author":"Fotos","year":"2013","journal-title":"Scand J Caring Sci"},{"key":"R3-20240828","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijcard.2008.05.047","article-title":"A propensity-matched study of low serum potassium and mortality in older adults with chronic heart failure","volume":"137","author":"Alper","year":"2009","journal-title":"Int J Cardiol"},{"key":"R4-20240828","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.ijcard.2011.06.121","article-title":"The meaning of hypokalemia in heart failure","volume":"158","author":"Bielecka-Dabrowa","year":"2012","journal-title":"Int J Cardiol"},{"key":"R5-20240828","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.jacc.2012.09.038","article-title":"Rehospitalization for heart failure: Problems and perspectives","volume":"61","author":"Gheorghiade","year":"2013","journal-title":"J Am Coll Cardiol"},{"key":"R6-20240828","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.amjmed.2008.06.031","article-title":"Clinical outcome of patients with heart failure and preserved left ventricular function","volume":"121","author":"Gotsman","year":"2008","journal-title":"Am J Med"},{"key":"R7-20240828","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1093\/europace\/euv204","article-title":"Ambulatory cardiac arrhythmias in relation to mild hypokalaemia and prognosis in community dwelling middle-aged and elderly subjects","volume":"18","author":"Mattsson","year":"2016","journal-title":"Europace"},{"key":"R8-20240828","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.amjmed.2012.06.037","article-title":"Electrolyte disorders in community subjects: Prevalence and risk factors","volume":"126","author":"Liamis","year":"2013","journal-title":"Am J Med"},{"key":"R9-20240828","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1038\/s41746-021-00550-0","article-title":"Point-of-care artificial intelligence-enabled ECG for dyskalemia: A retrospective cohort analysis for accuracy and outcome prediction","volume":"5","author":"Lin","year":"2022","journal-title":"NPJ Digit Med"},{"key":"R10-20240828","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jelectrocard.2019.09.005","article-title":"The relationship between serum potassium concentrations and electrocardiographic characteristics in 163,547 individuals from primary care","volume":"57","author":"Krogager","year":"2019","journal-title":"J Electrocardiol"},{"key":"R11-20240828","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1752-1947-4-115","article-title":"Pseudoinfarction pattern in a patient with hyperkalemia, diabetic ketoacidosis and normal coronary vessels: A case report","volume":"4","author":"Ziakas","year":"2010","journal-title":"J Med Case Rep"},{"key":"R13-20240828","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"R14-20240828","doi-asserted-by":"crossref","first-page":"A272","DOI":"10.1016\/S0735-1097(18)30813-1","article-title":"Non-invasive detection of hyperkalemia with a smartphone electrocardiogram and artificial intelligence","volume":"71","author":"Galloway","year":"2018","journal-title":"J Am Coll Cardiol"},{"key":"R15-20240828","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s10876-023-02480-1","article-title":"Synthesis of carbon quantum dots and Fe-doped carbon quantum dots as fluorescent probes via one-step microwave process for rapid and accurate detection of Diclofenac sodium","volume":"35","author":"Gholipour","year":"2023","journal-title":"J Cluster Sci"},{"key":"R16-20240828","first-page":"1","article-title":"The green synthesis of carbon quantum dots through one-step hydrothermal approach by orange juice for rapid, and accurate detection of dopamine","author":"Gholipour","year":"2023","journal-title":"J Fluoresc"},{"key":"R17-20240828","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1080\/1536383X.2023.2206124","article-title":"The synthesis of fluorescent carbon quantum dots for tartrazine detection in food: A novel one-step microwave heating approach","volume":"31","author":"Gholipour","year":"2023","journal-title":"Fullerenes, Nanotubes and Carbon Nanostructures"},{"key":"R18-20240828","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nat Methods"},{"key":"R19-20240828","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1001\/jamacardio.2019.0640","article-title":"Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram","volume":"4","author":"Galloway","year":"2019","journal-title":"JAMA Cardiol"},{"key":"R20-20240828","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s11739-019-02217-x","article-title":"Electrocardiographic T wave alterations and prediction of hyperkalemia in patients with acute kidney injury","volume":"15","author":"Regolisti","year":"2020","journal-title":"Intern Emerg Med"}],"container-title":["Journal of Family Medicine and Primary Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.ovid.com\/10.4103\/jfmpc.jfmpc_2025_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T13:58:14Z","timestamp":1787839094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ovid.com\/10.4103\/jfmpc.jfmpc_2025_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,26]]},"references-count":19,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.4103\/jfmpc.jfmpc_2025_23","relation":{},"ISSN":["2249-4863","2278-7135"],"issn-type":[{"value":"2249-4863","type":"print"},{"value":"2278-7135","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,26]]},"assertion":[{"value":"2023-12-28","name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-30","name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}}]}}