{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:16:24Z","timestamp":1774624584060,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11042-023-16028-y","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T06:02:21Z","timestamp":1687759341000},"page":"11831-11844","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Optimized support vector regression predicting treatment duration among tuberculosis patients in Malaysia"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6859-4488","authenticated-orcid":false,"given":"Vimala","family":"Balakrishnan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghayathri","family":"Ramanathan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chee Kuan","family":"Wong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"issue":"6","key":"16028_CR1","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1016\/j.puhe.2015.04.010","volume":"129","author":"M Atif","year":"2015","unstructured":"Atif M, Sulaiman SAS, Shafie AA, Babar ZU (2015) Duration of treatment in pulmonary tuberculosis: Are international guidelines on the management of tuberculosis missing something? Public Health 129(6):777\u2013782. https:\/\/doi.org\/10.1016\/j.puhe.2015.04.010","journal-title":"Public Health"},{"issue":"8","key":"16028_CR2","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1016\/j.amjmed.2006.08.033","volume":"120","author":"S Bangalore","year":"2007","unstructured":"Bangalore S, Kamalakkannan G, Parkar S, Messerli FH (2007) Fixed-Dose Combinations Improve Medication Compliance: A Meta-Analysis. Am J Med 120(8):713\u2013719. https:\/\/doi.org\/10.1016\/j.amjmed.2006.08.033","journal-title":"Am J Med"},{"issue":"1","key":"16028_CR3","first-page":"3","volume":"39","author":"P Bartholomay","year":"2016","unstructured":"Bartholomay P, Pelissari DM, de Araujo WN, Yadon ZE, Heldal E (2016) Quality of tuberculosis care at different levels of health care in Brazil in 2013. Rev. Panam. Salud Publica 39(1):3\u201311","journal-title":"Rev. Panam. Salud Publica"},{"key":"16028_CR4","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13:281\u2013305","journal-title":"J Mach Learn Res"},{"key":"16028_CR5","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.ebiom.2019.04.016","volume":"43","author":"ML Chen","year":"2019","unstructured":"Chen ML, Doddi A, Royer J, Freschi L, Schito M, Ezewudo M, Kohane IS, Beam A, Farhat M (2019) Beyond multidrug resistance: Leveraging rare variants with machine and statistical learning models in Mycobacterium tuberculosis resistance prediction. EBioMedicine 43:356\u2013369. https:\/\/doi.org\/10.1016\/j.ebiom.2019.04.016","journal-title":"EBioMedicine"},{"key":"16028_CR6","doi-asserted-by":"publisher","first-page":"106058","DOI":"10.1016\/j.cmpb.2021.106058","volume":"204","author":"S Govindarajan","year":"2021","unstructured":"Govindarajan S, Swaminathan R (2021) Extreme learning machine based differentiation of pulmonary tuberculosis in chest radiographs using integrated local feature descriptors. Comput Methods Programs Biomed 204:106058. https:\/\/doi.org\/10.1016\/j.cmpb.2021.106058","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"16028_CR7","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.amepre.2019.12.010","volume":"58","author":"MB Haddad","year":"2020","unstructured":"Haddad MB, Lash TL, Castro KG, Hill AN, Navin TR, Gandhi NR, Magee MJ (2020) Tuberculosis infection among people with diabetes: U.S. population differences by race\/ethnicity. Am J Prev Med 58(6):858\u2013863. https:\/\/doi.org\/10.1016\/j.amepre.2019.12.010","journal-title":"Am. J. Prev. Med."},{"key":"16028_CR8","doi-asserted-by":"publisher","first-page":"105536","DOI":"10.1016\/j.cmpb.2020.105536","volume":"195","author":"JC Huang","year":"2020","unstructured":"Huang JC, Tsai YC, Wu PY, Lien YH, Chien CY, Kuo CF, Hung JF, Chen SC, Kuo CH (2020) Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method. Comput Methods Programs Biomede 195:105536. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105536","journal-title":"Comput Methods Programs Biomede"},{"issue":"1","key":"16028_CR9","first-page":"52","volume":"18","author":"KK Kulwant","year":"2020","unstructured":"Kulwant KK, Said SM, Ismail SNS, Ying LP (2020) Risk factors of unfavourable TB treatment outcomes in Hulu Langat, Selangor. Malaysian J Med Health Sci 18(1):52\u201360","journal-title":"Malaysian J. Med. Health Sci"},{"issue":"1","key":"16028_CR10","doi-asserted-by":"publisher","first-page":"33","DOI":"10.4103\/jpcs.jpcs_8_18","volume":"4","author":"K Kumari","year":"2018","unstructured":"Kumari K, Yadav S (2018) Linear regression analysis study. Journal of the Practice of Cardiovascular Sciences 4(1):33. https:\/\/doi.org\/10.4103\/jpcs.jpcs_8_18","journal-title":"Journal of the Practice of Cardiovascular Sciences"},{"issue":"1","key":"16028_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0170104","volume":"12","author":"HH Lai","year":"2017","unstructured":"Lai HH, Lai YJ, Yen YF (2017) Association of body mass index with timing of death during tuberculosis treatment. PLoS ONE 12(1):1\u201312. https:\/\/doi.org\/10.1371\/journal.pone.0170104","journal-title":"PLoS ONE"},{"issue":"1","key":"16028_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12889-019-7969-5","volume":"20","author":"RBT Lim","year":"2020","unstructured":"Lim RBT, Wee WK, For WC, Ananthanarayanan JA, Soh YH, Goh LML, Tham DKT, Wong ML (2020) Correlates, facilitators and barriers of physical activity among primary care patients with prediabetes in Singapore - A mixed methods approach. BMC Public Health 20(1):1\u201313. https:\/\/doi.org\/10.1186\/s12889-019-7969-5","journal-title":"BMC Public Health"},{"issue":"5","key":"16028_CR13","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.jinf.2021.12.046","volume":"84","author":"Y Luo","year":"2022","unstructured":"Luo Y, Xue Y, Song H, Tang G, Liu W, Bai H, Yuan X, Tong S, Wang F, Cai Y, Sun Z (2022) Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection. J Infect 84(5):648\u2013657. https:\/\/doi.org\/10.1016\/j.jinf.2021.12.046","journal-title":"J Infect"},{"issue":"7","key":"16028_CR14","doi-asserted-by":"publisher","first-page":"e147","DOI":"10.1093\/cid\/ciw376","volume":"63","author":"P Nahid","year":"2016","unstructured":"Nahid P, Dorman SE, Alipanah N, Barry PM, Brozek JL, Cattamanchi A, Chaisson LH, Chaisson RE, Daley CL, Grzemska M, Higashi JM, Ho CS, Hopewell PC, Keshavjee SA, Lienhardt C, Menzies R, Merrifield C, Narita M, O\u2019Brien R, Vernon A (2016) Official American thoracic society\/centers for disease control and prevention\/infectious diseases society of America clinical practice guidelines: treatment of drug-susceptible tuberculosis. Clin Infectious Dis 63(7):e147\u2013e195. https:\/\/doi.org\/10.1093\/cid\/ciw376","journal-title":"Clin Infectious Dis"},{"issue":"11 SUPPL. 3","key":"16028_CR15","first-page":"292","volume":"3","author":"PY Norval","year":"1999","unstructured":"Norval PY, Blomberg B, Kitler ME, Dye C, Spinaci S (1999) Estimate of the global market for rifampicin-containing fixed-dose combination tablets. Int. J. Tuberc. Lung Dis 3(11 SUPPL. 3):292\u2013300","journal-title":"Int. J. Tuberc. Lung Dis"},{"issue":"4","key":"16028_CR16","doi-asserted-by":"publisher","first-page":"7","DOI":"10.5120\/ijca2017915495","volume":"175","author":"K Potdar","year":"2017","unstructured":"Potdar K, Pardawala TS, Pai DC (2017) A comparative study of categorical variable encoding techniques for neural network classifiers. Int J Comput Appl 175(4):7\u20139. https:\/\/doi.org\/10.5120\/ijca2017915495","journal-title":"Int. J. Comput. Appl"},{"issue":"11","key":"16028_CR17","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1128\/JCM.01013-17","volume":"55","author":"A Rosenthal","year":"2017","unstructured":"Rosenthal A, Gabrielian A, Engle E, Hurt DE, Alexandru S, Crudu V, Sergueev E, Kirichenko V, Lapitskii V, Snezhko E, Kovalev V, Astrovko A, Alena S, Taaffe J, Harris M, Long A, Wollenberg K, Akhundova I, Ismayilova S, Mindru R (2017) The TB portals: an open-access, web_based platform for global drug-resistant_tuberculosis data sharing and analysis. J Clin Microbiol 55(11):3267\u20133282","journal-title":"J. Clin. Microbiol"},{"issue":"2","key":"16028_CR18","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.cmpb.2013.04.018","volume":"111","author":"R Sa\u0142at","year":"2013","unstructured":"Sa\u0142at R, Sa\u0142at K (2013) The application of support vector regression for prediction of the antiallodynic effect of drug combinations in the mouse model of streptozocin-induced diabetic neuropathy. Comput Methods Programs Biomed 111(2):330\u2013337. https:\/\/doi.org\/10.1016\/j.cmpb.2013.04.018","journal-title":"Comput Methods Programs Biomed"},{"issue":"11","key":"16028_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0207491","volume":"13","author":"CM Sauer","year":"2018","unstructured":"Sauer CM, Sasson D, Paik KE, McCague N, Celi LA, Fern\u00e1ndez IS, Illigens BMW (2018) Feature selection and prediction of treatment failure in tuberculosis. PLoS ONE 13(11):1\u201314. https:\/\/doi.org\/10.1371\/journal.pone.0207491","journal-title":"PLoS ONE"},{"issue":"5","key":"16028_CR20","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1213\/ANE.0000000000002864","volume":"126","author":"P Schober","year":"2018","unstructured":"Schober P, Schwarte LA (2018) Correlation coefficients: Appropriate use and interpretation. Anesth Analg 126(5):1763\u20131768. https:\/\/doi.org\/10.1213\/ANE.0000000000002864","journal-title":"Anesth Analg"},{"key":"16028_CR21","doi-asserted-by":"publisher","first-page":"105418","DOI":"10.1016\/j.cmpb.2020.105418","volume":"191","author":"D Seo","year":"2020","unstructured":"Seo D, Kang E, Kim YM, Kim SY, Oh IS, Kim MG (2020) SVM-based waist circumference estimation using Kinect. Comput Methods Programs Biomed 191:105418. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105418","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"16028_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bjid.2022.102332","volume":"26","author":"A Sharma","year":"2022","unstructured":"Sharma A, Machado E, Lima KVB, Suffys PN, Concei\u00e7\u00e3o EC (2022) Tuberculosis drug resistance profiling based on machine learning: A literature review. Braz J Infect Dis 26(1):1\u20139. https:\/\/doi.org\/10.1016\/j.bjid.2022.102332","journal-title":"Braz J Infect Dis"},{"issue":"3","key":"16028_CR23","doi-asserted-by":"publisher","first-page":"5519","DOI":"10.32604\/cmc.2022.021666","volume":"70","author":"A Siddiqa","year":"2022","unstructured":"Siddiqa A, Naqvi SAZ, Ahsan M, Ditta A, Alquhayz H, Khan MA, Khan MA (2022) Robust length of stay prediction model for indoor patients. Computers, Materials and Continua 70(3):5519\u20135536. https:\/\/doi.org\/10.32604\/cmc.2022.021666","journal-title":"Computers, Materials and Continua"},{"issue":"2","key":"16028_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0228712","volume":"15","author":"H Singh","year":"2020","unstructured":"Singh H, Ramamohan V (2020) A model-based investigation into urban-rural disparities in tuberculosis treatment outcomes under the Revised National Tuberculosis Control Programme in India. PLoS ONE 15(2):1\u201315. https:\/\/doi.org\/10.1371\/journal.pone.0228712","journal-title":"PLoS ONE"},{"issue":"6","key":"16028_CR25","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1136\/amiajnl-2011-000755","volume":"19","author":"H Timimi","year":"2012","unstructured":"Timimi H, Falzon D, Glaziou P, Sismanidis C, Floyd K (2012) WHO guidance on electronic systems to manage data for tuberculosis care and control. J Am Med Inform Assoc 19(6):939\u2013941. https:\/\/doi.org\/10.1136\/amiajnl-2011-000755","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"16028_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0231986","volume":"15","author":"PSK Tok","year":"2020","unstructured":"Tok PSK, Liew SM, Wong LP, Razali A, Loganathan T, Chinna K, Ismail N, Kadir NA (2020) Determinants of unsuccessful treatment outcomes and mortality among tuberculosis patients in Malaysia: A registry-based cohort study. PLoS ONE 15(4):1\u201314. https:\/\/doi.org\/10.1371\/journal.pone.0231986","journal-title":"PLoS ONE"},{"key":"16028_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7502-7_101-1","author":"S Wang","year":"2016","unstructured":"Wang S, Tang J, Liu H (2016) Feature selection. Encyclopedia of Machine Learning and Data Mining, January. https:\/\/doi.org\/10.1007\/978-1-4899-7502-7_101-1","journal-title":"Encyclopedia of Machine Learning and Data Mining, January"},{"issue":"9","key":"16028_CR28","first-page":"1689","volume":"53","author":"World Health Organization","year":"2021","unstructured":"World Health Organization (2021) The end strategy TB. World Health Organization 53(9):1689\u20131699","journal-title":"World Health Organization"},{"key":"16028_CR29","doi-asserted-by":"publisher","unstructured":"Alsaffar M, Alshammari G, Alshammari A, Aljaloud S, Almurayziq TS, Hamad AA, Kumar V, Belay A (2021). Detection of tuberculosis disease using image processing technique. Mob Inf Syst, 2021. https:\/\/doi.org\/10.1155\/2021\/7424836","DOI":"10.1155\/2021\/7424836"},{"key":"16028_CR30","doi-asserted-by":"crossref","unstructured":"Althomsons SP, Winglee K, Heilig CM, Talarico S, Silk B, Wortham J, Hill AN, Navin TR (2022). Using machine learning techniques and national tuberculosis surveillance data to predict excess growth in genotyped tuberculosis clusters. AJR Am J Roentgenol 186(2), 227\u2013236. https:\/\/pubmed.ncbi.nlm.nih.gov\/28459981\/","DOI":"10.1093\/aje\/kwac117"},{"key":"16028_CR31","doi-asserted-by":"publisher","unstructured":"An L, Peng K, Yang X, Huang P, Luo Y, Feng P, Wei B (2022). Article E\u2010TBNet: Light Deep Neural Network for Automatic Detection of Tuberculosis with X\u2010ray DR Imaging. Sensors, 22(3). https:\/\/doi.org\/10.3390\/s22030821","DOI":"10.3390\/s22030821"},{"key":"16028_CR32","doi-asserted-by":"publisher","unstructured":"Asad M, Mahmood A, Usman M (2020). A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries. Tuberculosis (Edinburgh, Scotland), 123(June), 101944. https:\/\/doi.org\/10.1016\/j.tube.2020.101944","DOI":"10.1016\/j.tube.2020.101944"},{"key":"16028_CR33","doi-asserted-by":"publisher","unstructured":"Avoi R, Liaw YC (2021). Tuberculosis death epidemiology and its associated risk factors in sabah, malaysia. Int J Environ Health Res 18(18). https:\/\/doi.org\/10.3390\/ijerph18189740","DOI":"10.3390\/ijerph18189740"},{"key":"16028_CR34","doi-asserted-by":"publisher","unstructured":"Banga A, Ahuja R, Sharma SC (2021). Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities. International Journal of Systems Assurance Engineering and Management. https:\/\/doi.org\/10.1007\/s13198-020-01049-9","DOI":"10.1007\/s13198-020-01049-9"},{"key":"16028_CR35","unstructured":"Basak D, Pal S, Patranabis DC (2007). Support vector regression. Neural Information Processing-Letters and Reviews, 11."},{"key":"16028_CR36","doi-asserted-by":"publisher","unstructured":"Brindha GR, Rishiikeshwer BS, Santhi B, Nakendraprasath K, Manikandan R, Gandomi AH (2022). Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis. Comput Methods Programs Biomed, 224, 107027. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107027","DOI":"10.1016\/j.cmpb.2022.107027"},{"key":"16028_CR37","unstructured":"Brownlee J (2019). How to choose a feature selection method for machine learning. https:\/\/machinelearningmastery.com\/feature-selection-with-real-and-categorical-data\/"},{"key":"16028_CR38","unstructured":"Centers for Disease Control and Prevention (2020). Targeted TB Testing & Interpreting Skin Test Results. https:\/\/www.cdc.gov\/tb\/publications\/factsheets\/testing\/skintestresults.htm"},{"key":"16028_CR39","doi-asserted-by":"publisher","unstructured":"Iqbal A, Usman M, Ahmed Z (2022). An efficient deep learning-based framework for tuberculosis detection using chest X-ray images. Tuberculosis, 136(July), 102234. https:\/\/doi.org\/10.1016\/j.tube.2022.102234","DOI":"10.1016\/j.tube.2022.102234"},{"key":"16028_CR40","doi-asserted-by":"publisher","unstructured":"Karumbi J, Garner P (2015). Directly observed therapy for treating tuberculosis. Cochrane Database Syst. Rev, 2015(5). https:\/\/doi.org\/10.1002\/14651858.CD003343.pub4","DOI":"10.1002\/14651858.CD003343.pub4"},{"key":"16028_CR41","doi-asserted-by":"publisher","unstructured":"Meraj SS, Yaakob R, Azman A, Rum SNM, Nazri ASA (2019). Artificial intelligence in diagnosing tuberculosis: A review. International Journal on Advanced Science, Engineering and Information Technology, 9(1), 81\u201391. https:\/\/doi.org\/10.18517\/ijaseit.9.1.7567","DOI":"10.18517\/ijaseit.9.1.7567"},{"key":"16028_CR42","doi-asserted-by":"publisher","unstructured":"Mohidem NA, Osman M, Muharam FM, Elias SM, Shaharudin R, Hashim Z (2021). Prediction of tuberculosis cases based on sociodemographic and environmental factors in Gombak, Selangor, Malaysia: A Comparative Assessment of Multiple Linear Regression and Artificial Neural Network Models. Int. J. Microbiol. https:\/\/doi.org\/10.4103\/ijmy.ijmy","DOI":"10.4103\/ijmy.ijmy"},{"key":"16028_CR43","doi-asserted-by":"publisher","unstructured":"Natekin A, Knoll A (2013). Gradient boosting machines, a tutorial. Frontiers in Neurorobotics, 7(DEC). https:\/\/doi.org\/10.3389\/fnbot.2013.00021","DOI":"10.3389\/fnbot.2013.00021"},{"key":"16028_CR44","doi-asserted-by":"publisher","unstructured":"Pusch T, Pasipanodya JG, Hall RG, Gumbo T (2014). Therapy duration and long-term outcomes in extra-pulmonary tuberculosis. BMC Infectious Diseases, 14(1). https:\/\/doi.org\/10.1186\/1471-2334-14-115","DOI":"10.1186\/1471-2334-14-115"},{"key":"16028_CR45","doi-asserted-by":"publisher","unstructured":"Rajendran M, Zaki RA, Aghamohammadi N (2020). Contributing risk factors towards the prevalence of multidrug-resistant tuberculosis in Malaysia: A systematic review. Tuberculosis, 122(March), 101925. https:\/\/doi.org\/10.1016\/j.tube.2020.101925","DOI":"10.1016\/j.tube.2020.101925"},{"key":"16028_CR46","doi-asserted-by":"publisher","unstructured":"Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG, Bauck AE, Cifelli D, Smerek MM, Dickerson J, Laws RL, Madigan RA, Rusincovitch SA, Kluchar C, Califf RM (2013). Electronic health records based phenotyping in next-generation clinical trials: A perspective from the NIH health care systems collaboratory. J Am Med Inform Assoc, 20(E2). https:\/\/doi.org\/10.1136\/amiajnl-2013-001926","DOI":"10.1136\/amiajnl-2013-001926"},{"key":"16028_CR47","doi-asserted-by":"publisher","unstructured":"Rocha MS, Oliveira GP, Saraceni V, Aguiar FP, Coeli CM, Pinheiro RS (2018). Effect of inpatient and outpatient care on treatment outcome in tuberculosis: A cohort study. Rev Panam Salud Publica 42, 1\u20138. https:\/\/doi.org\/10.26633\/RPSP.2018.112","DOI":"10.26633\/RPSP.2018.112"},{"key":"16028_CR48","unstructured":"World Health Organization (2022). Drug-resistant TB. https:\/\/www.who.int\/teams\/global-tuberculosis-programme\/tb-reports\/global-tuberculosis-report-2022\/tb-disease-burden\/2-3-drug-resistant-tb"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16028-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16028-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16028-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T09:38:58Z","timestamp":1704879538000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16028-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,26]]},"references-count":48,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["16028"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16028-y","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,26]]},"assertion":[{"value":"4 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}