{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T22:17:14Z","timestamp":1769206634089,"version":"3.49.0"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,3,1]],"date-time":"2021-03-01T00:00:00Z","timestamp":1614556800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T00:00:00Z","timestamp":1613520000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100014055","name":"United States Army Medical Research Acquisition Activity","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014055","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence in Medicine"],"published-print":{"date-parts":[[2021,3]]},"DOI":"10.1016\/j.artmed.2021.102032","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T21:07:34Z","timestamp":1612991254000},"page":"102032","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care"],"prefix":"10.1016","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5349-6455","authenticated-orcid":false,"given":"Larry","family":"Hernandez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9172-4340","authenticated-orcid":false,"given":"Renaid","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neriman","family":"Tokcan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harm","family":"Derksen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5569-7904","authenticated-orcid":false,"given":"Ben E.","family":"Biesterveld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfred","family":"Croteau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron M.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Mathis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kayvan","family":"Najarian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Gryak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.artmed.2021.102032_bib0005","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1016\/j.athoracsur.2018.03.003","article-title":"The society of thoracic surgeons 2018 adult cardiac surgery risk models: part 2-statistical methods and results","volume":"105","author":"O\u2019Brien","year":"2018","journal-title":"Ann Thorac Surg"},{"issue":"12","key":"10.1016\/j.artmed.2021.102032_bib0010","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.1109\/TBME.2017.2684244","article-title":"A framework for patient state tracking by classifying multiscalar physiologic waveform features","volume":"64","author":"Vandendriessche","year":"2017","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"10.1016\/j.artmed.2021.102032_bib0015","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1097\/00003246-199704000-00010","article-title":"Poor prognosis for existing monitors in the intensive care unit","volume":"25","author":"Tsien","year":"1997","journal-title":"Crit Care Med"},{"issue":"12","key":"10.1016\/j.artmed.2021.102032_bib0020","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1007\/s001340051082","article-title":"Multicentric study of monitoring alarms in the adult intensive care unit (icu): a descriptive analysis","volume":"25","author":"Chambrin","year":"1999","journal-title":"Intensive Care Med"},{"issue":"5","key":"10.1016\/j.artmed.2021.102032_bib0025","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1213\/01.ane.0000204385.01983.61","article-title":"Alarm algorithms in critical care monitoring","volume":"102","author":"Imhoff","year":"2006","journal-title":"Anesth Analg"},{"issue":"6","key":"10.1016\/j.artmed.2021.102032_bib0030","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1097\/ACO.0b013e3282f10dff","article-title":"Should we be alarmed by our alarms?","volume":"20","author":"Hagenouw","year":"2007","journal-title":"Curr Opin Anesthesiol"},{"issue":"3","key":"10.1016\/j.artmed.2021.102032_bib0035","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.clp.2017.05.005","article-title":"Alarm safety and alarm fatigue","volume":"44","author":"Johnson","year":"2017","journal-title":"Clin Perinatol"},{"issue":"1","key":"10.1016\/j.artmed.2021.102032_bib0040","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1097\/DCC.0000000000000220","article-title":"Assessment of clinical alarms influencing nurses\u2019 perceptions of alarm fatigue","volume":"36","author":"Petersen","year":"2017","journal-title":"Dimens Crit Care Nurs"},{"key":"10.1016\/j.artmed.2021.102032_bib0045","doi-asserted-by":"crossref","DOI":"10.1155\/2015\/370194","article-title":"Big data analytics in healthcare","volume":"2015","author":"Belle","year":"2015","journal-title":"BioMed Res Int"},{"issue":"1","key":"10.1016\/j.artmed.2021.102032_bib0050","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1093\/bja\/ael331","article-title":"Integrated monitoring and analysis for early warning of patient deterioration","volume":"98","author":"Ismail","year":"2007","journal-title":"Br J Anaesth"},{"key":"10.1016\/j.artmed.2021.102032_bib0055","series-title":"49th IEEE conference on decision and control (CDC)","first-page":"4673","article-title":"Modeling and model predictive control of hemodynamic variables during hemodialysis","author":"Javed","year":"2010"},{"key":"10.1016\/j.artmed.2021.102032_bib0060","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2147\/OAEM.S178358","article-title":"Shock index in the emergency department: utility and limitations","volume":"11","author":"Koch","year":"2019","journal-title":"Open Access Emerg Med: OAEM"},{"issue":"8","key":"10.1016\/j.artmed.2021.102032_bib0065","doi-asserted-by":"crossref","first-page":"2350","DOI":"10.1109\/TBME.2013.2256423","article-title":"Real-time lumped parameter modeling of cardiovascular dynamics using electrocardiogram signals: toward virtual cardiovascular instruments","volume":"60","author":"Le","year":"2013","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"10.1016\/j.artmed.2021.102032_bib0070","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s10916-014-0038-9","article-title":"A clinical decision support system with an integrated emr for diagnosis of peripheral neuropathy","volume":"38","author":"Kunhimangalam","year":"2014","journal-title":"J Med Syst"},{"issue":"1","key":"10.1016\/j.artmed.2021.102032_bib0075","doi-asserted-by":"crossref","first-page":"S25","DOI":"10.1097\/TA.0b013e3182211601","article-title":"Use of advanced machine-learning techniques for noninvasive monitoring of hemorrhage","volume":"71","author":"Convertino","year":"2011","journal-title":"J Trauma Acute Care Surg"},{"issue":"1","key":"10.1016\/j.artmed.2021.102032_bib0080","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1186\/s13054-017-1874-z","article-title":"A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit","volume":"21","author":"Potes","year":"2017","journal-title":"Crit Care"},{"issue":"3","key":"10.1016\/j.artmed.2021.102032_bib0085","doi-asserted-by":"crossref","first-page":"e0118504","DOI":"10.1371\/journal.pone.0118504","article-title":"Automatic prediction of cardiovascular and cerebrovascular events using heart rate variability analysis","volume":"10","author":"Melillo","year":"2015","journal-title":"PLOS ONE"},{"issue":"8","key":"10.1016\/j.artmed.2021.102032_bib0090","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0134387","article-title":"Heart rate variability analysis in an experimental model of hemorrhagic shock and resuscitation in pigs","volume":"10","author":"Salom\u00e3o","year":"2015","journal-title":"PLOS ONE"},{"issue":"6","key":"10.1016\/j.artmed.2021.102032_bib0095","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10916-018-0942-5","article-title":"Toward hypertension prediction based on ppg-derived hrv signals: a feasibility study","volume":"42","author":"Lan","year":"2018","journal-title":"J Med Syst"},{"issue":"2","key":"10.1016\/j.artmed.2021.102032_bib0100","doi-asserted-by":"crossref","first-page":"166","DOI":"10.18632\/aging.101386","article-title":"Heart rate variability as predictive factor for sudden cardiac death","volume":"10","author":"Sessa","year":"2018","journal-title":"Aging (Albany NY)"},{"key":"10.1016\/j.artmed.2021.102032_bib0105","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jss.2017.11.029","article-title":"Spectral analysis of heart rate variability predicts mortality and instability from vascular injury","volume":"224","author":"Koko","year":"2018","journal-title":"J Surg Res"},{"issue":"6","key":"10.1016\/j.artmed.2021.102032_bib0110","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000014197","article-title":"Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department","volume":"98","author":"Chiew","year":"2019","journal-title":"Medicine"},{"issue":"2","key":"10.1016\/j.artmed.2021.102032_bib0115","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1007\/s12028-019-00684-w","article-title":"Admission heart rate variability is associated with fever development in patients with intracerebral hemorrhage","volume":"30","author":"Swor","year":"2019","journal-title":"Neurocrit Care"},{"issue":"6","key":"10.1016\/j.artmed.2021.102032_bib0120","doi-asserted-by":"crossref","first-page":"e19091","DOI":"10.2196\/19091","article-title":"Improvements in patient monitoring in the intensive care unit: survey study","volume":"22","author":"Poncette","year":"2020","journal-title":"J Med Internet Res"},{"issue":"2","key":"10.1016\/j.artmed.2021.102032_bib0125","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0148544","article-title":"A signal processing approach for detection of hemodynamic instability before decompensation","volume":"11","author":"Belle","year":"2016","journal-title":"PLOS ONE"},{"key":"10.1016\/j.artmed.2021.102032_bib0130","volume":"vol. 8","author":"Barlow","year":"1972"},{"key":"10.1016\/j.artmed.2021.102032_bib0135","first-page":"1","article-title":"Local extremes, runs, strings and multiresolution","author":"Davies","year":"2001","journal-title":"Ann Stat"},{"issue":"6","key":"10.1016\/j.artmed.2021.102032_bib0140","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TSP.2007.916129","article-title":"On the dual-tree complex wavelet packet and M-band transforms","volume":"56","author":"Bayram","year":"2008","journal-title":"IEEE Trans Signal Process"},{"key":"10.1016\/j.artmed.2021.102032_bib0145","series-title":"BP_annotate","author":"Laurin","year":"2019"},{"key":"10.1016\/j.artmed.2021.102032_bib0150","series-title":"The severity of stages estimation during hemorrhage using error correcting output codes method, VCU digital archives","author":"Luo","year":"2012"},{"key":"10.1016\/j.artmed.2021.102032_bib0155","first-page":"122","article-title":"Implications of factor analysis of three-way matrices for measurement of change","volume":"15","author":"Tucker","year":"1963","journal-title":"Probl Meas Change"},{"key":"10.1016\/j.artmed.2021.102032_bib0160","article-title":"The extension of factor analysis to three-dimensional matrices","volume":"110119","author":"Tucker","year":"1964","journal-title":"Contrib Math Psychol"},{"issue":"4","key":"10.1016\/j.artmed.2021.102032_bib0165","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1137\/S0895479896305696","article-title":"A multilinear singular value decomposition","volume":"21","author":"De Lathauwer","year":"2000","journal-title":"SIAM J Matrix Anal Appl"},{"issue":"3","key":"10.1016\/j.artmed.2021.102032_bib0170","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev"},{"issue":"6","key":"10.1016\/j.artmed.2021.102032_bib0175","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2512329","article-title":"Most tensor problems are NP-hard","volume":"60","author":"Hillar","year":"2013","journal-title":"J ACM (JACM)"},{"issue":"11","key":"10.1016\/j.artmed.2021.102032_bib0180","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1080\/14786440109462720","article-title":"LIII. on lines and planes of closest fit to systems of points in space","volume":"2","author":"Pearson","year":"1901","journal-title":"Lond Edinb Dublin Philos Mag J Sci"},{"issue":"3\/4","key":"10.1016\/j.artmed.2021.102032_bib0185","doi-asserted-by":"crossref","first-page":"321","DOI":"10.2307\/2333955","article-title":"Relations between two sets of variates","volume":"28","author":"Hotelling","year":"1936","journal-title":"Biometrika"},{"issue":"02","key":"10.1016\/j.artmed.2021.102032_bib0190","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1142\/S0219720005001004","article-title":"Minimum redundancy feature selection from microarray gene expression data","volume":"3","author":"Ding","year":"2005","journal-title":"J Bioinform Comput Biol"},{"issue":"8","key":"10.1016\/j.artmed.2021.102032_bib0195","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"10.1016\/j.artmed.2021.102032_bib0200","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"issue":"3","key":"10.1016\/j.artmed.2021.102032_bib0205","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1145\/321075.321084","article-title":"Automatic indexing: an experimental inquiry","volume":"8","author":"Maron","year":"1961","journal-title":"J ACM (JACM)"},{"issue":"3","key":"10.1016\/j.artmed.2021.102032_bib0210","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"issue":"771-780","key":"10.1016\/j.artmed.2021.102032_bib0215","first-page":"1612","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"J-Jpn Soc Artif Intell"},{"issue":"5","key":"10.1016\/j.artmed.2021.102032_bib0220","doi-asserted-by":"crossref","first-page":"442","DOI":"10.3390\/e21050442","article-title":"Learning using concave and convex kernels: applications in predicting quality of sleep and level of fatigue in fibromyalgia","volume":"21","author":"Sabeti","year":"2019","journal-title":"Entropy"},{"issue":"December","key":"10.1016\/j.artmed.2021.102032_bib0225","first-page":"1889","article-title":"Working set selection using second order information for training support vector machines","volume":"6","author":"Fan","year":"2005","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.artmed.2021.102032_bib0230","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine","author":"Friedman","year":"2001","journal-title":"Ann Stat"},{"key":"10.1016\/j.artmed.2021.102032_bib0235","series-title":"Correlation-based feature selection for machine learning","author":"Hall","year":"1999"}],"container-title":["Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365721000257?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0933365721000257?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T15:10:45Z","timestamp":1760541045000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0933365721000257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3]]},"references-count":47,"alternative-id":["S0933365721000257"],"URL":"https:\/\/doi.org\/10.1016\/j.artmed.2021.102032","relation":{},"ISSN":["0933-3657"],"issn-type":[{"value":"0933-3657","type":"print"}],"subject":[],"published":{"date-parts":[[2021,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multimodal tensor-based method for integrative and continuous patient monitoring during postoperative cardiac care","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence in Medicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artmed.2021.102032","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102032"}}