{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:05:13Z","timestamp":1790615113200,"version":"4.1.0"},"reference-count":82,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Cardiovascular diseases (CVD) are a leading global health challenge, particularly in resource-constrained settings where delayed diagnosis worsens outcomes. Abnormal heart rate (tachycardia\/bradycardia) is a modifiable, independent risk factor that benefits from early, automated detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used a large real-world dataset from Hospital Santa Maria, Lisbon (23,122 patients, January 2019\u2013November 2021, spanning the COVID-19 period). Two operational outcome definitions were evaluated: (i) an interquartile-range (IQR)-based, patient-specific definition, used to compare four classifiers (Logistic Regression, K-Nearest Neighbors, Random Forest, Naive Bayes), and (ii) standard clinical thresholds for tachycardia\/bradycardia, applied to the best-performing model. Following CRISP-DM, models were trained and evaluated with patient-grouped, walk-forward time-series cross-validation to prevent patient-level data leakage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Logistic Regression was the best-performing model. Under the IQR-based definition: AUROC 0.78, recall 85.4%, precision 66.1%, F1-score 74.5%. Under the clinically standard threshold-based definition: AUROC 0.81, recall 91.7%, precision 68.8%, F1-score 78.6%. Expanded metrics (event prevalence, false-positive rate, alert burden, calibration, confidence intervals) are reported. A prototype real-time e-mail alert system was demonstrated as a feasibility proof-of-concept and reviewed informally by two cardiovascular specialists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Recall and AUROC alone do not establish clinical utility. Key limitations include the single-centre design, substantial missingness in ethnicity\/diagnosis variables, the small clinician-evaluation sample (n = 2), and pandemic-period data collection. This work is presented as a methodologically strengthened proof-of-concept, with prospective, multi-site, multi-clinician external validation identified as the necessary next step.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1879609","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:34:31Z","timestamp":1788845671000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based early detection of abnormal heart rate in critically ill patients: a real-world clinical dataset study with automated alert system integration"],"prefix":"10.3389","volume":"9","author":[{"given":"Joao C.","family":"Ferreira","sequence":"first","affiliation":[{"name":"(ISCTE-IUL) \u2013 ISTAR-IUL, Information Sciences, Technologies and Architecture Research Center, University Institute of Lisbon (ISCTE)","place":["Lisbon, Portugal"]},{"name":"Faculty of Logistics, Molde University College \u2013 Specialized University in Logistics, Hogskolen i Molde","place":["Molde, Norway"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis B.","family":"Elvas","sequence":"additional","affiliation":[{"name":"(ISCTE-IUL) \u2013 ISTAR-IUL, Information Sciences, Technologies and Architecture Research Center, University Institute of Lisbon (ISCTE)","place":["Lisbon, Portugal"]},{"name":"Department of Logistics, Molde University College","place":["Molde, Norway"]},{"name":"INOV INESC Inova\u00e7\u00e3o \u2013 Instituto de Novas Tecnologias","place":["Lisbon, Portugal"]},{"name":"Breast Cancer Research Program, Champalimaud Foundation","place":["Lisbon, Portugal"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.19153\/CLEIej.24.3.5","article-title":"Using machine learning to support health system planning during the COVID-19 pandemic: a case study using data from S\u00e3o Jos\u00e9 dos Campos (Brazil)","volume":"24","author":"Abuabara","year":"2021","journal-title":"CLEI Electron J."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"105540","DOI":"10.1016\/j.compbiomed.2022.105540","article-title":"ECG-ICOVIDNet: interpretable AI model to identify changes in the ECG signals of post-COVID subjects","volume":"146","author":"Agrawal","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1108\/IR-10-2021-0240","article-title":"Industry-4.0 oriented predictive analytics of cardiovascular diseases using machine learning, hyperparameter tuning and ensemble techniques","volume":"49","author":"Ahamed","year":"2022","journal-title":"Ind. Robot."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13721-020-00272-5","article-title":"Classification of heart sound short records using bispectrum analysis approach images and deep learning","volume":"9","author":"Alqudah","year":"2020","journal-title":"Netw Model Anal Health Inform Bioinforma."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"9915","DOI":"10.1016\/j.jksuci.2021.12.019","article-title":"Effect of Hilbert-Huang transform on classification of PCG signals using machine learning","volume":"34","author":"Arslan","year":"2022","journal-title":"J King Saud Univ Comput Inf Sci."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"299","DOI":"10.3390\/bios12050299","article-title":"An intelligent ECG-based tool for diagnosing COVID-19 via ensemble deep learning techniques","volume":"12","author":"Attallah","year":"2022","journal-title":"Biosensors"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/03772063.2015.1009403","article-title":"Detection of heart muscle damage from automated analysis of echocardiogram video","volume":"61","author":"Balaji","year":"2015","journal-title":"IETE J. Res."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"17313","DOI":"10.1038\/s41598-022-20176-w","article-title":"Identifying pre-existing conditions and multimorbidity patterns associated with in-hospital mortality in patients with COVID-19","volume":"12","author":"Bucholc","year":"2022","journal-title":"Sci. Rep."},{"key":"ref9","doi-asserted-by":"publisher","first-page":"4151","DOI":"10.3390\/app12094151","article-title":"Automated classification of left ventricular hypertrophy on cardiac MRI","volume":"12","author":"Budai","year":"2022","journal-title":"Appl. Sci."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"e001852","DOI":"10.1136\/openhrt-2021-001852","article-title":"Novel tech throws knock-out punch to ECG improving GP referral decisions to cardiology","volume":"9","author":"Calcagno","year":"2022","journal-title":"Open Heart"},{"key":"ref9001","year":"2022"},{"key":"ref9002","year":"2022"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"035012","DOI":"10.1088\/2057-1976\/ac6b53","article-title":"Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders","volume":"9","author":"Chaitanya","year":"2023","journal-title":"Biomed. Phys. Eng. Express"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"e23948","DOI":"10.2196\/23948","article-title":"A multimodality machine learning approach to differentiate severe and nonsevere COVID-19: model development and validation","volume":"23","author":"Chen","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"e019905","DOI":"10.1161\/JAHA.120.019905","article-title":"Deep learning algorithm for automated cardiac murmur detection via a digital stethoscope platform","volume":"10","author":"Chorba","year":"2021","journal-title":"J. Am. Heart Assoc."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"2885","DOI":"10.1093\/eurheartj\/ehab153","article-title":"Electrocardiogram screening for aortic valve stenosis using artificial intelligence","volume":"42","author":"Cohen-Shelly","year":"2021","journal-title":"Eur. Heart J."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.3390\/pathogens11111281","article-title":"Tomography and prognostic indices in the state of the art of evaluation in hospitalized patients with COVID-19 pneumonia","volume":"11","author":"Criales-Vera","year":"2022","journal-title":"Pathogens"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.jjcc.2013.02.018","article-title":"Heart rate: a global target for cardiovascular disease and therapy along the cardiovascular disease continuum","volume":"62","author":"Custodis","year":"2013","journal-title":"J. Cardiol."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"e24478","DOI":"10.2196\/24478","article-title":"Computing SARS-CoV-2 infection risk from symptoms, imaging, and test data: diagnostic model development","volume":"22","author":"D\u2019Ambrosia","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"e19739","DOI":"10.2196\/19739","article-title":"An application of machine learning to etiological diagnosis of secondary hypertension: retrospective study using electronic medical records","volume":"9","author":"Diao","year":"2021","journal-title":"JMIR Med. Inform."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"012027","DOI":"10.1088\/1742-6596\/2273\/1\/012027","article-title":"Predicting heart diseases through feature selection and ensemble classifiers","volume":"2273","author":"Diwan","year":"2022","journal-title":"J. Phys. Conf. 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Physiol."},{"key":"ref25","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1002\/med.21731","article-title":"Liquid biopsy technologies for hematological diseases","volume":"41","author":"Fu","year":"2021","journal-title":"Med. Res. Rev."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1002\/ejhf.2140","article-title":"Diagnosis and treatment of cardiac amyloidosis. A position statement of the European Society of Cardiology Working Group on myocardial and pericardial diseases","volume":"23","author":"Garcia-Pavia","year":"2021","journal-title":"Eur. J. Heart Fail."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"8843963","DOI":"10.1155\/2020\/8843963","article-title":"Deep layer kernel sparse representation network for the detection of heart valve ailments from the time-frequency representation of PCG recordings","volume":"2020","author":"Ghosh","year":"2020","journal-title":"Biomed. Res. 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Biomed. Health Inform."},{"key":"ref38","author":"Kamala","year":"2022"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"334","DOI":"10.3348\/kjr.2020.0099","article-title":"Performance of prediction models for diagnosing severe aortic stenosis based on aortic valve calcium on cardiac computed tomography: incorporation of radiomics and machine learning","volume":"22","author":"Kang","year":"2021","journal-title":"Korean J. Radiol."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1186\/s12872-020-01620-z","article-title":"Identifying phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning","volume":"20","author":"Kaptein","year":"2020","journal-title":"BMC Cardiovasc. Disord."},{"key":"ref41","volume-title":"Handbook of Cardiovascular Behavioral Medicine","author":"Kapuku","year":"2022"},{"key":"ref42","doi-asserted-by":"publisher","first-page":"103445","DOI":"10.1016\/j.bspc.2021.103445","article-title":"Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features","volume":"73","author":"Khan","year":"2022","journal-title":"Biomed Signal Process Control."},{"key":"ref43","doi-asserted-by":"publisher","first-page":"715072","DOI":"10.3389\/fimmu.2021.715072","article-title":"Artificial intelligence predicts severity of COVID-19 based on correlation of exaggerated monocyte activation, excessive organ damage and hyperinflammatory syndrome: a prospective clinical study","volume":"12","author":"Krysko","year":"2021","journal-title":"Front. Immunol."},{"key":"ref44","doi-asserted-by":"publisher","first-page":"236","DOI":"10.5455\/aim.2022.30.236-250","article-title":"The recent advances of Mobile healthcare in cardiology practice","volume":"30","author":"Kulbayeva","year":"2022","journal-title":"Acta Inform Med."},{"key":"ref45","doi-asserted-by":"publisher","first-page":"85","DOI":"10.4997\/JRCPE.2021.122","article-title":"The value of physical examination in the era of telemedicine","volume":"51","author":"Lee","year":"2021","journal-title":"J. R. Coll. Physicians Edinb."},{"key":"ref46","doi-asserted-by":"crossref","DOI":"10.25080\/Majora-92bf1922-00a","article-title":"Data structures for statistical computing in Python","author":"McKinney","year":"2010"},{"key":"ref47","doi-asserted-by":"publisher","first-page":"6355","DOI":"10.3390\/s22176355","article-title":"Smart diagnostics: combining artificial intelligence and in vitro diagnostics","volume":"22","author":"McRae","year":"2022","journal-title":"Sensors"},{"key":"ref48","author":"Mohandas","year":"2021"},{"key":"ref49","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s40959-021-00111-0","article-title":"TAVR and cancer: machine learning-augmented propensity score mortality and cost analysis in over 30 million patients","volume":"7","author":"Monlezun","year":"2021","journal-title":"Cardiooncology"},{"key":"ref50","doi-asserted-by":"publisher","first-page":"100062","DOI":"10.1016\/j.ibmed.2022.100062","article-title":"Artificial intelligence-enabled predictive model of progression from moderate to severe aortic stenosis","volume":"6","author":"Moualla","year":"2022","journal-title":"Intell Based Med."},{"key":"ref51","doi-asserted-by":"publisher","first-page":"8430565","DOI":"10.1155\/2021\/8430565","article-title":"Time-frequency analysis of EEG signals and GLCM features for depth of anesthesia monitoring","volume":"2021","author":"Mousavi","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref52","doi-asserted-by":"publisher","first-page":"1312","DOI":"10.52586\/5026","article-title":"Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review","volume":"26","author":"Munjral","year":"2021","journal-title":"Front. Biosci. (Landmark Ed)"},{"key":"ref53","doi-asserted-by":"publisher","first-page":"n160","DOI":"10.1136\/bmj.n160","article-title":"PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref54","doi-asserted-by":"publisher","first-page":"768","DOI":"10.1016\/j.ciresp.2021.08.005","article-title":"The impact of COVID-19 in patients with severe aortic stenosis: artificial intelligence research","volume":"100","author":"Pascual-Tejerina","year":"2021","journal-title":"Cir Esp (Engl Ed)."},{"key":"ref55","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.593847","article-title":"statsmodels\/statsmodels: release 0.14.0","author":"Perktold","year":"2023","journal-title":"Zenodo"},{"key":"ref57","doi-asserted-by":"publisher","first-page":"19450","DOI":"10.1038\/s41598-021-98773-4","article-title":"Cardiac involvement in hospitalized patients with COVID-19 and its incremental value in outcomes prediction","volume":"11","author":"Pournazari","year":"2021","journal-title":"Sci. Rep."},{"key":"ref58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-021-00169-1","article-title":"COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network","volume":"10","author":"Rahman","year":"2022","journal-title":"Health Inf Sci Syst."},{"key":"ref59","volume-title":"Foundations of Secure Computation","author":"Rivest","year":"1978"},{"key":"ref60","doi-asserted-by":"publisher","first-page":"5268","DOI":"10.3390\/ijerph19095268","article-title":"Telemonitoring in long-COVID patients\u2014preliminary findings","volume":"19","author":"Romaszko-Wojtowicz","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref61","doi-asserted-by":"publisher","first-page":"1672677","DOI":"10.1155\/2022\/1672677","article-title":"Detection of cardiovascular disease based on PPG signals using machine learning with cloud computing","volume":"2022","author":"Sadad","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref62","doi-asserted-by":"publisher","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","article-title":"The precision-recall plot is more informative than the ROC plot for imbalanced datasets","volume":"10","author":"Saito","year":"2015","journal-title":"PLoS One"},{"key":"ref63","author":"Shi","year":"2021"},{"key":"ref64","doi-asserted-by":"publisher","first-page":"23817","DOI":"10.1038\/s41598-021-03441-2","article-title":"Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters","volume":"11","author":"Shokouhmand","year":"2021","journal-title":"Sci. Rep."},{"key":"ref65","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.ahj.2018.07.001","article-title":"The National Echocardiography Database Australia (NEDA): rationale and methodology","volume":"204","author":"Strange","year":"2018","journal-title":"Am. Heart J."},{"key":"ref66","doi-asserted-by":"publisher","first-page":"6818","DOI":"10.1038\/s41467-022-34405-3","article-title":"Faecal microbiome-based machine learning for multi-class disease diagnosis","volume":"13","author":"Su","year":"2022","journal-title":"Nat. Commun."},{"key":"ref67","author":"Suhas","year":"2017"},{"key":"ref68","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1038\/s41746-021-00461-0","article-title":"Patient-specific COVID-19 resource utilization prediction using fusion AI model","volume":"4","author":"Tariq","year":"2021","journal-title":"NPJ Digit Med"},{"key":"ref69","doi-asserted-by":"publisher","first-page":"1712","DOI":"10.1136\/heartjnl-2019-315183","article-title":"Positron emission tomography imaging in cardiovascular disease","volume":"106","author":"Tarkin","year":"2020","journal-title":"Heart"},{"key":"ref70","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.bspc.2018.05.008","article-title":"A novel heart-mobile interface for detection and classification of heart sounds","volume":"45","author":"Thiyagaraja","year":"2018","journal-title":"Biomed Signal Process Control"},{"key":"ref71","author":"Thomas","year":"2020"},{"key":"ref72","doi-asserted-by":"publisher","first-page":"100059","DOI":"10.1016\/j.ibmed.2022.100059","article-title":"Artificial intelligence to assist physicians in identifying patients with severe aortic stenosis","volume":"6","author":"Thomas","year":"2022","journal-title":"Intell Based Med."},{"key":"ref73","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/s00246-018-2036-z","article-title":"Artificial intelligence-assisted auscultation of heart murmurs: validation by virtual clinical trial","volume":"40","author":"Thompson","year":"2019","journal-title":"Pediatr. Cardiol."},{"key":"ref74","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: the convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref75","doi-asserted-by":"publisher","first-page":"86","DOI":"10.3390\/jcdd9030086","article-title":"Transfer learning models for detecting six categories of phonocardiogram recordings","volume":"9","author":"Wang","year":"2022","journal-title":"J Cardiovasc Dev Dis."},{"key":"ref76","author":"Wei","year":"2019"},{"key":"ref9004","year":"2023"},{"key":"ref77","doi-asserted-by":"publisher","first-page":"6063","DOI":"10.1007\/s10462-021-09969-z","article-title":"Detection of heart valve disorders from PCG signals using TCWT, FA-MVEMD, Shannon energy envelope and deterministic learning","volume":"54","author":"Zeng","year":"2021","journal-title":"Artif. Intell. 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