{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T23:08:05Z","timestamp":1777158485944,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s11517-022-02570-8","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T09:03:01Z","timestamp":1651654981000},"page":"1881-1896","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Efficient analysis of COVID-19 clinical data using machine learning models"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8121-2168","authenticated-orcid":false,"given":"Sarwan","family":"Ali","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yijing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Murray","family":"Patterson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"2570_CR1","doi-asserted-by":"crossref","unstructured":"Ali S, Patterson M. Spike2vec: An efficient and scalable embedding approach for covid-19 spike sequences. In 2021 IEEE International Conference on Big Data (Big Data) 2021 Dec 15 (pp. 1533\u20131540).","DOI":"10.1109\/BigData52589.2021.9671848"},{"key":"2570_CR2","unstructured":"Ali S, Bello B, Patterson M (2021a) Classifying covid-19 spike sequences from geographic location using deep learning. arXiv preprint arXiv:211000809"},{"key":"2570_CR3","unstructured":"GISAID Website (Accessed: 10-12-2021) . https:\/\/www.gisaidorg\/"},{"key":"2570_CR4","doi-asserted-by":"crossref","unstructured":"Leung CK, Chen Y, Hoi CS, Shang S, Cuzzocrea A (2020a) Machine learning and olap on big covid-19 data. In: 2020 IEEE International Conference on Big Data (Big Data), pp 5118\u20135127","DOI":"10.1109\/BigData50022.2020.9378407"},{"key":"2570_CR5","doi-asserted-by":"crossref","unstructured":"Leung CK, Chen Y, Shang S, Deng D (2020b) Big data science on covid-19 data. In: 2020 IEEE 14th International Conference on Big Data Science and Engineering (BigDataSE), pp 14\u201321","DOI":"10.1109\/BigDataSE50710.2020.00010"},{"key":"2570_CR6","doi-asserted-by":"crossref","unstructured":"Ali S, Mansoor H, Arshad N, Khan I (2019a) Short term load forecasting using smart meter data. In: International Conference on Future Energy Systems, pp 419\u2013421","DOI":"10.1145\/3307772.3330173"},{"key":"2570_CR7","doi-asserted-by":"crossref","unstructured":"Ali S, Mansoor H, Khan I, Arshad N, Khan MA, Faizullah S (2019b) Short-term load forecasting using ami data. arXiv preprint arXiv:191212479","DOI":"10.1145\/3307772.3330173"},{"key":"2570_CR8","doi-asserted-by":"crossref","unstructured":"Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, Khanna A (2021) Realizing an effective covid-19 diagnosis system based on machine learning and iot in smart hospital environment. IEEE Internet of Things Journal","DOI":"10.1109\/JIOT.2021.3050775"},{"key":"2570_CR9","unstructured":"Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds) Advances in Neural Information Processing Systems 30, pp 4765\u20134774"},{"issue":"10","key":"2570_CR10","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DKW, Newman SF, Kim J et al (2018) Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2(10):749","journal-title":"Nat Biomed Eng"},{"issue":"1","key":"2570_CR11","doi-asserted-by":"publisher","first-page":"2522","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee SI (2020) From local explanations to global understanding with explainable ai for trees. Nat Mach Intell 2(1):2522\u20135839","journal-title":"Nat Mach Intell"},{"issue":"2","key":"2570_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3442390","volume":"12","author":"S Ali","year":"2021","unstructured":"Ali S, Shakeel MH, Khan I, Faizullah S, Khan MA (2021) Predicting attributes of nodes using network structure. ACM Trans Intell Syst Technol 12(2):1\u201323","journal-title":"ACM Trans Intell Syst Technol"},{"key":"2570_CR13","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: International Conference on Knowledge Discovery & Data Mining (KDD), pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"2570_CR14","doi-asserted-by":"crossref","unstructured":"Yang L, Guo Y, Cao X (2018) Multi-facet network embedding: Beyond the general solution of detection and representation. In: AAAI Conference on Artificial Intelligence (AAAI), pp 499\u2013506","DOI":"10.1609\/aaai.v32i1.11247"},{"key":"2570_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110120","volume":"140","author":"TB Alakus","year":"2020","unstructured":"Alakus TB, Turkoglu I (2020) Comparison of deep learning approaches to predict covid-19 infection. Chaos, Solitons & Fractals 140:110120","journal-title":"Chaos, Solitons & Fractals"},{"key":"2570_CR16","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.ins.2020.01.037","volume":"519","author":"M Ahmad","year":"2020","unstructured":"Ahmad M, Ali S, Tariq J, Khan I, Shabbir M, Zaman A (2020) Combinatorial trace method for network immunization.\u00a0Inf Sci\u00a0519:215\u2013228","journal-title":"Information Sciences"},{"key":"2570_CR17","doi-asserted-by":"crossref","unstructured":"Ullah A, Ali S, Khan I, Khan MA, Faizullah S (2020) Effect of analysis window and feature selection on classification of hand movements using EMG signal. In: SAI Intelligent Systems Conference (IntelliSys), pp 400\u2013415","DOI":"10.1007\/978-3-030-55190-2_30"},{"key":"2570_CR18","doi-asserted-by":"crossref","unstructured":"Shakeel MH, Karim A, Khan I (2019) A multi-cascaded deep model for bilingual sms classification. In: International Conference on Neural Information Processing, pp 287\u2013298","DOI":"10.1007\/978-3-030-36708-4_24"},{"key":"2570_CR19","doi-asserted-by":"crossref","unstructured":"Shakeel MH, Faizullah S, Alghamidi T, Khan I (2020a) Language independent sentiment analysis. In: 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), pp 1\u20135","DOI":"10.1109\/AECT47998.2020.9194186"},{"issue":"3","key":"2570_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102204","volume":"57","author":"MH Shakeel","year":"2020","unstructured":"Shakeel MH, Karim A, Khan I (2020b) A multi-cascaded model with data augmentation for enhanced paraphrase detection in short texts. Information Processing & Management 57(3):102204","journal-title":"Information Processing & Management"},{"key":"2570_CR21","doi-asserted-by":"crossref","unstructured":"Hassan IU, Haseeb A, Ali S (2021) Locally weighted mean phase angle (lwmpa) based tone mapping quality index (tmqi-3). Accepted at: International Conference on Intelligent Vision and Computing (ICIVC)","DOI":"10.1007\/978-3-030-97196-0_13"},{"key":"2570_CR22","doi-asserted-by":"crossref","unstructured":"Leung CK, Fung DL, Mushtaq SB, Leduchowski OT, Bouchard RL, Jin H, Cuzzocrea A, Zhang CY (2020c) Data science for healthcare predictive analytics. In: Proceedings of the 24th Symposium on International Database Engineering & Applications, pp 1\u201310","DOI":"10.1145\/3410566.3410598"},{"key":"2570_CR23","doi-asserted-by":"crossref","unstructured":"Ali S, Sahoo B, Ullah N, Zelikovskiy A, Patterson M, Khan I (2021d) A k-mer based approach for sars-cov-2 variant identification. In: International Symposium on Bioinformatics Research and Applications, pp 153\u2013164","DOI":"10.1007\/978-3-030-91415-8_14"},{"key":"2570_CR24","doi-asserted-by":"crossref","unstructured":"Ali S, Ali TE, Khan MA, Khan I, Patterson M. Effective and scalable clustering of SARS-CoV-2 sequences. In 2021 the 5th International Conference on Big Data Research (ICBDR) 2021 Sep 25 (pp. 42\u201349).","DOI":"10.1145\/3505745.3505752"},{"issue":"12","key":"2570_CR25","doi-asserted-by":"publisher","first-page":"348","DOI":"10.3390\/a14120348","volume":"14","author":"Z Tayebi","year":"2021","unstructured":"Tayebi Z, Ali S, Patterson M (2021) Robust representation and efficient feature selection allows for effective clustering of sars-cov-2 variants. Algorithms 14(12):348","journal-title":"Algorithms"},{"issue":"3","key":"2570_CR26","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.bbrc.2020.09.010","volume":"533","author":"K Kuzmin","year":"2020","unstructured":"Kuzmin K, Adeniyi AE, DaSouza Jr AK, Lim D, Nguyen H, Molina NR, Xiong L, Weber IT, Harrison RW (2020) Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.\u00a0Biochem Biophys Res Commun\u00a0533(3), 553\u2013558","journal-title":"Biochemical and Biophysical Research Communications"},{"issue":"3","key":"2570_CR27","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s10140-020-01886-y","volume":"28","author":"V Shah","year":"2021","unstructured":"Shah V, Keniya R, Shridharani A, Punjabi M, Shah J, Mehendale N (2021) Diagnosis of covid-19 using ct scan images and deep learning techniques.\u00a0Emerg Radiol\u00a028(3):497\u2013505","journal-title":"Emergency radiology"},{"issue":"2","key":"2570_CR28","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/bioengineering8020026","volume":"8","author":"P Zaffino","year":"2021","unstructured":"Zaffino P, Marzullo A, Moccia S, Calimeri F, De Momi E, Bertucci B, Arcuri PP, Spadea MF (2021) An open-source covid-19 ct dataset with automatic lung tissue classification for radiomics. Bioengineering 8(2):26","journal-title":"Bioengineering"},{"key":"2570_CR29","doi-asserted-by":"crossref","unstructured":"Teli MN (2021) Telinet: Classifying ct scan images for covid-19 diagnosis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 496\u2013502","DOI":"10.1109\/ICCVW54120.2021.00060"},{"key":"2570_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.110190","volume":"140","author":"H Panwar","year":"2020","unstructured":"Panwar H, Gupta P, Siddiqui MK, Morales-Menendez R, Bhardwaj P, Singh V (2020) A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images. Chaos, Solitons & Fractals 140:110190","journal-title":"Chaos, Solitons & Fractals"},{"key":"2570_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-020-01582-x","volume":"44","author":"AS Albahri","year":"2020","unstructured":"Albahri AS, Hamid RA, Alwan JK, Al-Qays Z, Zaidan A, Zaidan B, Albahri A, AlAmoodi A, Khlaf JM, Almahdi E, et al. (2020) Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (covid-19): a systematic review.\u00a0J Med Syst\u00a044:1\u201311","journal-title":"Journal of medical systems"},{"issue":"1","key":"2570_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-1002-x","volume":"20","author":"WT Li","year":"2020","unstructured":"Li WT, Ma J, Shende N, Castaneda G, Chakladar J, Tsai JC, Apostol L, Honda CO, Xu J, Wong LM, et al. (2020) Using machine learning of clinical data to diagnose covid-19: a systematic review and meta-analysis. BMC medical informatics and decision making 20(1):1\u201313","journal-title":"BMC medical informatics and decision making"},{"key":"2570_CR33","doi-asserted-by":"crossref","unstructured":"Fung DL, Hoi CS, Leung CK, Zhang CY (2021) Predictive analytics of covid-19 with neural networks. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9534188"},{"key":"2570_CR34","unstructured":"Ali S (2021) Cache replacement algorithm. arXiv preprint arXiv:210714646"},{"issue":"11","key":"2570_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Rudnicki WR, et al. (2010) Feature selection with the boruta package. J Stat Softw 36(11), 1\u201313","journal-title":"J Stat Softw"},{"issue":"2","key":"2570_CR36","first-page":"105","volume":"4","author":"AE Hoerl","year":"1975","unstructured":"Hoerl AE, Kannard RW, Baldwin KF (1975) Ridge regression: some simulations. Communications in Statistics-Theory and Methods 4(2), 105\u2013123","journal-title":"Communications in Statistics-Theory and Methods"},{"key":"2570_CR37","unstructured":"Rahimi A, Recht B, et\u00a0al. (2007) Random features for large-scale kernel machines. In: NIPS, vol\u00a03, p\u00a05"},{"issue":"11","key":"2570_CR38","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1089\/cmb.2021.0271","volume":"28","author":"S Ali","year":"2021","unstructured":"Ali S, Ciccolella S, Lucarella L, Vedova GD, Patterson M (2021b) Simpler and faster development of tumor phylogeny pipelines.\u00a0J Comput Biol\u00a028(11), 1142\u20131155","journal-title":"Journal of Computational Biology"},{"issue":"1","key":"2570_CR39","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1002\/wics.14","volume":"1","author":"GC McDonald","year":"2009","unstructured":"McDonald GC (2009) Ridge regression. Wiley Interdisciplinary Reviews:\u00a0Comput Stat\u00a01(1), 93\u2013100","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"2570_CR40","first-page":"1","volume-title":"London","author":"P Devijver","year":"1982","unstructured":"Devijver P, Kittler J (1982) Pattern recognition: A statistical approach. In: London, GB: Prentice-Hall, pp 1\u2013448"},{"key":"2570_CR41","unstructured":"Van der M L, Hinton G (2008) Visualizing data using t-SNE.\u00a0J Mach Learn Res\u00a0(JMLR) 9(11)"},{"key":"2570_CR42","unstructured":"NewYork Times (NYT) (2021) https:\/\/www.nytimes.com\/interactive\/2020\/us\/covid-19-vaccine-doses.html, [Online; Accessed: 15-12-2021]"},{"key":"2570_CR43","doi-asserted-by":"crossref","unstructured":"Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing, pp 1\u20134","DOI":"10.1007\/978-3-642-00296-0_5"},{"key":"2570_CR44","doi-asserted-by":"crossref","unstructured":"Myers L, Sirois MJ. Spearman correlation coefficients, differences between. Encyclopedia of statistical sciences. 2004 Jul 15;12.","DOI":"10.1002\/0471667196.ess5050"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02570-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02570-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02570-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T19:06:14Z","timestamp":1727118374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02570-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":44,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["2570"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02570-8","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,4]]},"assertion":[{"value":"17 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}