{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T15:31:12Z","timestamp":1771342272788,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00521-024-09600-6","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T13:02:38Z","timestamp":1708520558000},"page":"10109-10122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A hybrid computational approach to process real-time streaming multi-sources data and improve classification for emergency patients triage services: moving forward to an efficient IoMT-based real-time telemedicine systems"],"prefix":"10.1007","volume":"36","author":[{"given":"Omar Sadeq","family":"Salman","sequence":"first","affiliation":[]},{"given":"Nurul Mu\u2019azzah","family":"Abdul Latiff","sequence":"additional","affiliation":[]},{"given":"Omar H.","family":"Salman","sequence":"additional","affiliation":[]},{"given":"Sharifah Hafizah","family":"Syed Ariffin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"issue":"S1","key":"9600_CR1","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1017\/S0266462318002829","volume":"34","author":"P Galvan","year":"2018","unstructured":"Galvan P et al (2018) PP155 telemedicine enhance universal coverage of diagnostic services. Int J Technol Assess Health Care 34(S1):127","journal-title":"Int J Technol Assess Health Care"},{"key":"9600_CR2","doi-asserted-by":"crossref","unstructured":"Fran\u00e7a RP, Iano Y, Monteiro ACB,Arthur R (2020) Potential proposal to improve data transmission in healthcare systems. in Deep Learning Techniques for Biomedical and Health Informatics, Elsevier, pp. 267\u2013283","DOI":"10.1016\/B978-0-12-819061-6.00011-2"},{"issue":"3","key":"9600_CR3","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s12553-021-00547-5","volume":"11","author":"ZA Abdalkareem","year":"2021","unstructured":"Abdalkareem ZA, Amir A, Al-Betar MA, Ekhan P, Hammouri AI (2021) Healthcare scheduling in optimization context: a review. Health Technol (Berl) 11(3):445\u2013469. https:\/\/doi.org\/10.1007\/s12553-021-00547-5","journal-title":"Health Technol (Berl)"},{"issue":"8","key":"9600_CR4","doi-asserted-by":"publisher","first-page":"6185","DOI":"10.1007\/s00521-022-07998-5","volume":"35","author":"AHAOS Albahri","year":"2023","unstructured":"Albahri AHAOS, Alsattar AAZHA, Albahri BBZAS (2023) Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment. Neural Comput Appl 35(8):6185\u20136196. https:\/\/doi.org\/10.1007\/s00521-022-07998-5","journal-title":"Neural Comput Appl"},{"issue":"9","key":"9600_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-014-0103-4","volume":"38","author":"OH Salman","year":"2014","unstructured":"Salman OH, Rasid MFA, Saripan MI, Subramaniam SK (2014) Multi-sources data fusion framework for remote triage prioritization in telehealth. J Med Syst 38(9):1\u201323. https:\/\/doi.org\/10.1007\/s10916-014-0103-4","journal-title":"J Med Syst"},{"issue":"5","key":"9600_CR6","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1016\/j.cjca.2020.12.004","volume":"37","author":"NRC Campbell","year":"2021","unstructured":"Campbell NRC et al (2021) WHO HEARTS: a global program to reduce cardiovascular disease burden: experience implementing in the Americas and opportunities in Canada. Can J Cardiol 37(5):744\u2013755","journal-title":"Can J Cardiol"},{"issue":"6","key":"9600_CR7","doi-asserted-by":"publisher","first-page":"e005375","DOI":"10.1161\/CIRCOUTCOMES.118.005375","volume":"12","author":"AN Nowbar","year":"2019","unstructured":"Nowbar AN, Gitto M, Howard JP, Francis DP, Al-Lamee R (2019) Mortality from ischemic heart disease: analysis of data from the World Health Organization and coronary artery disease risk factors From NCD Risk Factor Collaboration. Circ Cardiovasc Qual outcomes 12(6):e005375","journal-title":"Circ Cardiovasc Qual outcomes"},{"key":"9600_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00401-x","volume":"2","author":"SMA Ullah","year":"2021","unstructured":"Ullah SMA, Islam MM, Mahmud S, Nooruddin S, Raju SMTU, Haque MR (2021) Scalable telehealth services to combat novel coronavirus (COVID-19) pandemic. SN Comput Sci 2:1\u20138","journal-title":"SN Comput Sci"},{"issue":"8","key":"9600_CR9","doi-asserted-by":"publisher","first-page":"2195","DOI":"10.3390\/s20082195","volume":"20","author":"F Jamil","year":"2020","unstructured":"Jamil F, Ahmad S, Iqbal N, Kim D-H (2020) Towards a remote monitoring of patient vital signs based on IoT-based blockchain integrity management platforms in smart hospitals. Sensors 20(8):2195","journal-title":"Sensors"},{"key":"9600_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2020.103592","author":"OH Salman","year":"2020","unstructured":"Salman OH, Aal-Nouman MI, Taha ZK (2020) Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: toward scalable and efficient real time healthcare monitoring syste. J Biomed Inform. https:\/\/doi.org\/10.1016\/j.jbi.2020.103592","journal-title":"J Biomed Inform"},{"issue":"4","key":"9600_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-0916-7","volume":"42","author":"N Kalid","year":"2018","unstructured":"Kalid N et al (2018) Based on real time remote health monitoring systems: a new approach for prioritization \u2018large scales data\u2019 patients with chronic heart diseases using body sensors and communication technology. J Med Syst 42(4):1\u201337","journal-title":"J Med Syst"},{"issue":"5","key":"9600_CR12","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1142\/S0219622017500225","volume":"16","author":"OH Salman","year":"2017","unstructured":"Salman OH, Zaidan AA, Zaidan BB, Naserkalid, Hashim M (2017) Novel methodology for triage and prioritizing using \u2018big data\u2019 patients with chronic heart diseases through telemedicine environmental. Int J Inf Technol Decis Mak 16(5):1211\u20131245. https:\/\/doi.org\/10.1142\/S0219622017500225","journal-title":"Int J Inf Technol Decis Mak"},{"issue":"19","key":"9600_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s19194283","volume":"19","author":"JNS Rub\u00ed","year":"2019","unstructured":"Rub\u00ed JNS, Gondim PRL (2019) IoMT platform for pervasive healthcare data aggregation, processing, and sharing based on oneM2M and openEHR. Sensors (Switzerland) 19(19):1\u201325. https:\/\/doi.org\/10.3390\/s19194283","journal-title":"Sensors (Switzerland)"},{"key":"9600_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-017-0883-4","volume":"42","author":"N Kalid","year":"2018","unstructured":"Kalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Muzammil H (2018) Based real time remote health monitoring systems: a review on patients prioritization and related\" big data\" using body sensors information and communication technology. J Med Syst 42:1\u201330. https:\/\/doi.org\/10.1007\/s10916-017-0883-4","journal-title":"J Med Syst"},{"key":"9600_CR15","doi-asserted-by":"publisher","first-page":"91521","DOI":"10.1109\/ACCESS.2020.2994746","volume":"8","author":"KI Mohammed","year":"2020","unstructured":"Mohammed KI et al (2020) A uniform intelligent prioritisation for solving diverse and big data generated from multiple chronic diseases patients based on hybrid decision-making and voting method. IEEE Access 8:91521\u201391530. https:\/\/doi.org\/10.1109\/ACCESS.2020.2994746","journal-title":"IEEE Access"},{"key":"9600_CR16","doi-asserted-by":"publisher","first-page":"105151","DOI":"10.1016\/j.cmpb.2019.105151","volume":"185","author":"KI Mohammed","year":"2020","unstructured":"Mohammed KI et al (2020) Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases. Comput Methods Programs Biomed 185:105151","journal-title":"Comput Methods Programs Biomed"},{"key":"9600_CR17","doi-asserted-by":"publisher","first-page":"4333","DOI":"10.1007\/s12652-021-03325-3","volume":"13","author":"RA Hamid","year":"2021","unstructured":"Hamid RA, Albahri AS, Albahri OS, Zaidan AA (2021) Dempster\u2013Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases. J Ambient Intell Humaniz Comput 13:4333","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"3","key":"9600_CR18","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1002\/inf2.12094","volume":"2","author":"A Chen","year":"2020","unstructured":"Chen A, Zhang X, Zhou Z (2020) Machine learning: Accelerating materials development for energy storage and conversion. InfoMat 2(3):553\u2013576. https:\/\/doi.org\/10.1002\/inf2.12094","journal-title":"InfoMat"},{"key":"9600_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bjps\/axz035","volume":"73","author":"E Sullivan","year":"2022","unstructured":"Sullivan E (2022) Understanding from machine learning models. Br J Philos Sci 73:1\u201334","journal-title":"Br J Philos Sci"},{"key":"9600_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106357","author":"OH Salman","year":"2021","unstructured":"Salman OH, Taha Z, Alsabah MQ, Hussein YS, Mohammed AS, Aal-Nouman M (2021) A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. Comput Methods Programs Biomed. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106357","journal-title":"Comput Methods Programs Biomed"},{"issue":"10","key":"9600_CR21","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/s10916-021-01762-3","volume":"45","author":"IR Mendo","year":"2021","unstructured":"Mendo IR, Marques G, de la Torre D\u00edez I, L\u00f3pez-Coronado M, Mart\u00edn-Rodr\u00edguez F (2021) Machine learning in medical emergencies: a systematic review and analysis. J Med Syst 45(10):88","journal-title":"J Med Syst"},{"key":"9600_CR22","doi-asserted-by":"publisher","first-page":"105291","DOI":"10.1016\/j.compag.2020.105291","volume":"170","author":"J Astill","year":"2020","unstructured":"Astill J, Dara RA, Fraser EDG, Roberts B, Sharif S (2020) Smart poultry management: smart sensors, big data, and the internet of things. Comput Electron Agric 170:105291","journal-title":"Comput Electron Agric"},{"issue":"4","key":"9600_CR23","doi-asserted-by":"publisher","first-page":"e0174708","DOI":"10.1371\/journal.pone.0174708","volume":"12","author":"S Horng","year":"2018","unstructured":"Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA (2018) Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE 12(4):e0174708","journal-title":"PLoS ONE"},{"issue":"3","key":"9600_CR24","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1016\/j.jksuci.2020.01.010","volume":"34","author":"SP Chatrati","year":"2022","unstructured":"Chatrati SP et al (2022) Smart home health monitoring system for predicting type 2 diabetes and hypertension. J King Saud Univ Comput Inf Sci 34(3):862\u2013870. https:\/\/doi.org\/10.1016\/j.jksuci.2020.01.010","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"9600_CR25","doi-asserted-by":"publisher","DOI":"10.36287\/ijmsit.6.1.86","author":"\u015e Bora","year":"2022","unstructured":"Bora \u015e, Kantarc\u0131 A, Erdo\u011fan A, Beynek B, Kheibari B, Evren V (2022) Machine learning for E-triage. Int J Multidiscip Stud Innov Technol. https:\/\/doi.org\/10.36287\/ijmsit.6.1.86","journal-title":"Int J Multidiscip Stud Innov Technol"},{"issue":"19","key":"9600_CR26","doi-asserted-by":"publisher","first-page":"7227","DOI":"10.3390\/s22197227","volume":"22","author":"CAU Hassan","year":"2022","unstructured":"Hassan CAU, Iqbal J, Irfan R, Hussain S, Algarni AD, Bukhari SSH, Alturki N, Ullah SS (2022) Effectively predicting the presence of coronary heart disease using machine learning classifiers. Sensors 22(19):7227","journal-title":"Sensors"},{"key":"9600_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1472-6947-14-75","volume":"14","author":"N Liu","year":"2014","unstructured":"Liu N et al (2014) Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection. BMC Med Inform Decis Mak 14:1\u20139","journal-title":"BMC Med Inform Decis Mak"},{"issue":"12","key":"9600_CR28","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1111\/acem.13655","volume":"25","author":"SJ Patel","year":"2018","unstructured":"Patel SJ, Chamberlain DB, Chamberlain JM (2018) A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med 25(12):1463\u20131470","journal-title":"Acad Emerg Med"},{"issue":"1","key":"9600_CR29","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/s13721-022-00407-w","volume":"12","author":"SY Kadum","year":"2023","unstructured":"Kadum SY et al (2023) Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services. Netw Model Anal Heal Informatics Bioinforma 12(1):11. https:\/\/doi.org\/10.1007\/s13721-022-00407-w","journal-title":"Netw Model Anal Heal Informatics Bioinforma"},{"key":"9600_CR30","doi-asserted-by":"publisher","DOI":"10.3390\/bios12080562","author":"P Manickam","year":"2022","unstructured":"Manickam P et al (2022) Artificial intelligence (AI ) and Internet of Medical Things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors. https:\/\/doi.org\/10.3390\/bios12080562","journal-title":"Biosensors"},{"key":"9600_CR31","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/2487759","author":"MF Khan","year":"2021","unstructured":"Khan MF et al (2021) An IoMT-enabled smart healthcare model to monitor elderly people using machine learning technique. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2021\/2487759","journal-title":"Comput Intell Neurosci"},{"issue":"14056","key":"9600_CR32","doi-asserted-by":"publisher","first-page":"14056","DOI":"10.1109\/ACCESS.2017.2726543","volume":"5","author":"JKP Seng","year":"2017","unstructured":"Seng JKP, Ang KLM (2017) Big feature data analytics: split and combine linear discriminant analysis (SC-LDA) for integration towards decision making analytics. IEEE Access 5(14056):14056\u201314065. https:\/\/doi.org\/10.1109\/ACCESS.2017.2726543","journal-title":"IEEE Access"},{"key":"9600_CR33","doi-asserted-by":"publisher","first-page":"baaa010","DOI":"10.1093\/database\/baaa010","volume":"2020","author":"Z Ahmed","year":"2020","unstructured":"Ahmed Z, Mohamed K, Zeeshan S, Dong X (2020) Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020:baaa010","journal-title":"Database"},{"key":"9600_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13561-0","author":"P Pradeepa","year":"2022","unstructured":"Pradeepa P, Jeyakumar MK (2022) Modelling of IDBN with LSNN based optimal feature selection for the prediction of CKD using real time data. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-022-13561-0","journal-title":"Multimed Tools Appl"},{"key":"9600_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi9040272","author":"H Cao","year":"2020","unstructured":"Cao H, Wachowicz M (2020) A holistic overview of anticipatory learning for the internet of moving things: research challenges and opportunities. ISPRS Int J Geo-Inf. https:\/\/doi.org\/10.3390\/ijgi9040272","journal-title":"ISPRS Int J Geo-Inf"},{"key":"9600_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2020.112412","author":"X Jin","year":"2020","unstructured":"Jin X, Liu C, Xu T, Su L, Zhang X (2020) Artificial intelligence biosensors: challenges and prospects. Biosens Bioelectron. https:\/\/doi.org\/10.1016\/j.bios.2020.112412","journal-title":"Biosens Bioelectron"},{"key":"9600_CR37","doi-asserted-by":"publisher","first-page":"102996","DOI":"10.1016\/j.micpro.2020.102996","volume":"73","author":"B Gomatheeshwari","year":"2020","unstructured":"Gomatheeshwari B, Selvakumar J (2020) Appropriate allocation of workloads on performance asymmetric multicore architectures via deep learning algorithms. Microprocess Microsyst 73:102996. https:\/\/doi.org\/10.1016\/j.micpro.2020.102996","journal-title":"Microprocess Microsyst"},{"key":"9600_CR38","doi-asserted-by":"publisher","first-page":"54776","DOI":"10.1109\/ACCESS.2020.2980942","volume":"8","author":"GT Reddy","year":"2020","unstructured":"Reddy GT, Reddy MPK, Lakshmanna K, Kaluri R, Rajput DS (2020) Analysis of dimensionality reduction techniques on big data. IEEE Access 8:54776\u201354788. https:\/\/doi.org\/10.1109\/ACCESS.2020.2980942","journal-title":"IEEE Access"},{"issue":"August","key":"9600_CR39","doi-asserted-by":"publisher","first-page":"85333","DOI":"10.1109\/ACCESS.2023.3303810","volume":"11","author":"TP Raptis","year":"2023","unstructured":"Raptis TP, Passarella A (2023) A survey on networked data streaming with Apache Kafka. IEEE Access 11(August):85333\u201385350. https:\/\/doi.org\/10.1109\/ACCESS.2023.3303810","journal-title":"IEEE Access"},{"key":"9600_CR40","doi-asserted-by":"publisher","first-page":"106576","DOI":"10.1016\/j.dib.2020.106576","volume":"34","author":"OH Salman","year":"2021","unstructured":"Salman OH, Aal-nouman MI, Taha ZK, Alsabah MQ, Hussein YS, Abdelkareem ZA (2021) Formulating multi diseases dataset for identifying, triaging and prioritizing patients to multi medical emergency levels: simulated dataset accompanied with codes. Data Br 34:106576. https:\/\/doi.org\/10.1016\/j.dib.2020.106576","journal-title":"Data Br"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09600-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09600-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09600-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T08:09:51Z","timestamp":1716797391000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09600-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,21]]},"references-count":40,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9600"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09600-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,21]]},"assertion":[{"value":"15 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}