{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:40:06Z","timestamp":1765280406360,"version":"3.37.3"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71972012","72202217"],"award-info":[{"award-number":["71972012","72202217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10479-023-05660-4","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T12:01:54Z","timestamp":1700222514000},"page":"621-645","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integration of text-mining and telemedicine appointment optimization"],"prefix":"10.1007","volume":"341","author":[{"given":"Menglei","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Mosaffa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5220-6025","authenticated-orcid":false,"given":"Amir","family":"Ardestani-Jaafari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinlin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"5660_CR1","doi-asserted-by":"crossref","unstructured":"Adnan, K., Rehan A., Siak\u00a0W. K., & Adnan B. A. A. (2020). Role and challenges of unstructured big data in healthcare. Data Management, Analytics and Innovation 301\u2013323.","DOI":"10.1007\/978-981-32-9949-8_22"},{"key":"5660_CR2","doi-asserted-by":"crossref","unstructured":"Ahmadi-Javid, A., Jalali, Z., & Klassen, K. J. (2017). Outpatient appointment systems in healthcare: A review of optimization studies. European Journal of Operational Research, 258(1), 3\u201334.","DOI":"10.1016\/j.ejor.2016.06.064"},{"key":"5660_CR3","doi-asserted-by":"crossref","unstructured":"Ardestani-Jaafari, A., & Delage, E. (2016). Robust optimization of sums of piecewise linear functions with application to inventory problems. Operations research, 64(2), 474\u2013494.","DOI":"10.1287\/opre.2016.1483"},{"issue":"3","key":"5660_CR4","doi-asserted-by":"publisher","first-page":"201","DOI":"10.5455\/aim.2018.26.201-206","volume":"26","author":"A Asiri","year":"2018","unstructured":"Asiri, A., AlBishi, S., AlMadani, W., ElMetwally, A., & Househ, M. (2018). The use of telemedicine in surgical care: a systematic review. Acta Informatica Medica, 26(3), 201.","journal-title":"Acta Informatica Medica"},{"issue":"3","key":"5660_CR5","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.ejor.2019.05.008","volume":"281","author":"C Baechle","year":"2020","unstructured":"Baechle, C., Derrick Huang, C., Agarwal, A., Behara, R. S., & Goo, J. (2020). Latent topic ensemble learning for hospital readmission cost optimization. European Journal of Operational Research, 281(3), 517\u2013531.","journal-title":"European Journal of Operational Research"},{"issue":"4","key":"5660_CR6","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.jamcollsurg.2019.05.029","volume":"229","author":"MA Bartek","year":"2019","unstructured":"Bartek, M. A., Saxena, R. C., Solomon, S., Fong, C. T., Behara, L. D., Venigandla, R., Velagapudi, K., Lang, J. D., & Nair, B. G. (2019). Improving operating room efficiency: machine learning approach to predict case-time duration. Journal of the American College of Surgeons, 229(4), 346\u2013354.","journal-title":"Journal of the American College of Surgeons"},{"issue":"4","key":"5660_CR7","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1287\/ijoc.2017.0750","volume":"29","author":"BP Berg","year":"2017","unstructured":"Berg, B. P., & Denton, B. T. (2017). Fast approximation methods for online scheduling of outpatient procedure centers. INFORMS Journal on Computing, 29(4), 631\u2013644.","journal-title":"INFORMS Journal on Computing"},{"key":"5660_CR8","doi-asserted-by":"crossref","unstructured":"Bertsimas, D., Jean P., Jennifer S., & Manu, T. (2021). Predicting inpatient flow at a major hospital using interpretable analytics. Manufacturing & Service Operations Management .","DOI":"10.1101\/2020.05.12.20098848"},{"issue":"1","key":"5660_CR9","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1287\/opre.1050.0238","volume":"54","author":"D Bertsimas","year":"2006","unstructured":"Bertsimas, D., & Thiele, A. (2006). A robust optimization approach to inventory theory. Operations research, 54(1), 150\u2013168.","journal-title":"Operations research"},{"issue":"6","key":"5660_CR10","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1287\/msom.2020.0908","volume":"23","author":"\u00d6E \u00c7ak\u0131c\u0131","year":"2021","unstructured":"\u00c7ak\u0131c\u0131, \u00d6. E., & Mills, A. F. (2021). On the role of teletriage in healthcare demand management. Manufacturing & Service Operations Management, 23(6), 1483\u20131504.","journal-title":"Manufacturing & Service Operations Management"},{"key":"5660_CR11","doi-asserted-by":"crossref","unstructured":"Chawla, N. V., Bowyer, K. W., Hall, L. O., Philip, W., & Kegelmeyer. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research,16, 321\u2013357.","DOI":"10.1613\/jair.953"},{"key":"5660_CR12","volume-title":"Introducing data science: big data, machine learning, and more, using Python tools","author":"D Cielen","year":"2016","unstructured":"Cielen, D., & Meysman, A. (2016). Introducing data science: big data, machine learning, and more, using Python tools. Simon and Schuster."},{"issue":"1","key":"5660_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1357633X0501100102","volume":"11","author":"J Craig","year":"2005","unstructured":"Craig, J., & Petterson, V. (2005). Introduction to the practice of telemedicine. Journal of telemedicine and telecare, 11(1), 3\u20139.","journal-title":"Journal of telemedicine and telecare"},{"issue":"2","key":"5660_CR14","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.ejor.2016.02.040","volume":"253","author":"TG Crainic","year":"2016","unstructured":"Crainic, T. G., Gobbato, L., Perboli, G., & Rei, W. (2016). Logistics capacity planning: A stochastic bin packing formulation and a progressive hedging meta-heuristic. European Journal of Operational Research, 253(2), 404\u2013417.","journal-title":"European Journal of Operational Research"},{"key":"5660_CR15","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.sbspro.2014.01.099","volume":"111","author":"TG Crainic","year":"2014","unstructured":"Crainic, T. G., Gobbato, L., Perboli, G., Rei, W., Watson, J.-P., & Woodruff, D. L. (2014). Bin packing problems with uncertainty on item characteristics: An application to capacity planning in logistics. Procedia-Social and Behavioral Sciences, 111, 654\u2013662.","journal-title":"Procedia-Social and Behavioral Sciences"},{"issue":"5","key":"5660_CR16","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1287\/msom.2019.0778","volume":"22","author":"T Dai","year":"2020","unstructured":"Dai, T., & Tayur, S. (2020). Om forum-healthcare operations management: a snapshot of emerging research. Manufacturing & Service Operations Management, 22(5), 869\u2013887.","journal-title":"Manufacturing & Service Operations Management"},{"issue":"10","key":"5660_CR17","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1089\/tmj.2009.0067","volume":"15","author":"ME D\u00e1valos","year":"2009","unstructured":"D\u00e1valos, M. E., French, M. T., Burdick, A. E., & Simmons, S. C. (2009). Economic evaluation of telemedicine: review of the literature and research guidelines for benefit-cost analysis. Telemedicine and e-Health, 15(10), 933\u2013948.","journal-title":"Telemedicine and e-Health"},{"key":"5660_CR18","unstructured":"Delana, K., Sarang D., Kamalini, R., Ganesh-Babu,\u00a0B. S., & Thulasiraj, R. (2022). Multichannel delivery in healthcare: the impact of telemedicine centers in southern india. Management Science ."},{"key":"5660_CR19","doi-asserted-by":"crossref","unstructured":"Denton, Brian\u00a0T, Andrew\u00a0J Miller, Hari\u00a0J Balasubramanian, & Todd\u00a0R Huschka. (2010). Optimal allocation of surgery blocks to operating rooms under uncertainty. Operations research58(4-part-1) 802\u2013816.","DOI":"10.1287\/opre.1090.0791"},{"issue":"3","key":"5660_CR20","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1287\/serv.2018.0222","volume":"10","author":"SA Erdogan","year":"2018","unstructured":"Erdogan, S. A., Krupski, T. L., & Lobo, J. M. (2018). Optimization of telemedicine appointments in rural areas. Service Science, 10(3), 261\u2013276.","journal-title":"Service Science"},{"issue":"1","key":"5660_CR21","doi-asserted-by":"publisher","DOI":"10.2196\/21327","volume":"7","author":"A Garg","year":"2021","unstructured":"Garg, A., Goyal, S., Thati, R., Thati, N., et al. (2021). Implementation of telemedicine in a tertiary hospital-based ambulatory practice in detroit during the covid-19 pandemic: observational study. JMIR Public Health and Surveillance, 7(1), e21327.","journal-title":"JMIR Public Health and Surveillance"},{"key":"5660_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2022.106087","volume":"151","author":"M Goerigk","year":"2023","unstructured":"Goerigk, M., & Kurtz, J. (2023). Data-driven robust optimization using deep neural networks. Computers & Operations Research, 151, 106087.","journal-title":"Computers & Operations Research"},{"issue":"1","key":"5660_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-02165-7","volume":"10","author":"Y Goldberg","year":"2017","unstructured":"Goldberg, Y. (2017). Neural network methods for natural language processing. Synthesis lectures on human language technologies, 10(1), 1\u2013309.","journal-title":"Synthesis lectures on human language technologies"},{"key":"5660_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104591","volume":"156","author":"D Golmohammadi","year":"2021","unstructured":"Golmohammadi, D. (2021). A decision-making tool based on historical data for service time prediction in outpatient scheduling. International Journal of Medical Informatics, 156, 104591.","journal-title":"International Journal of Medical Informatics"},{"key":"5660_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijdrr.2021.102100","volume":"55","author":"A Gulzari","year":"2021","unstructured":"Gulzari, A., & Tarakci, H. (2021). A healthcare location-allocation model with an application of telemedicine for an earthquake response phase. International journal of disaster risk reduction, 55, 102100.","journal-title":"International journal of disaster risk reduction"},{"issue":"4","key":"5660_CR26","first-page":"1551","volume":"33","author":"C Guo","year":"2021","unstructured":"Guo, C., Bodur, M., Aleman, D. M., & Urbach, D. R. (2021). Logic-based benders decomposition and binary decision diagram based approaches for stochastic distributed operating room scheduling. INFORMS Journal on Computing, 33(4), 1551\u20131569.","journal-title":"INFORMS Journal on Computing"},{"key":"5660_CR27","unstructured":"Hearty, & John. (2016). Advanced machine learning with Python. Packt Publishing Ltd."},{"key":"5660_CR28","unstructured":"Henan Proviance. & (2021). Henan province medical service specification. http:\/\/www.zdsfy.net\/ueditor\/php\/upload\/file\/20210702\/1625185563852459.pdf."},{"key":"5660_CR29","doi-asserted-by":"crossref","unstructured":"Hindle, G., Kunc, M., Mortensen, M., & Oztekin, A. (2020). Richard Vidgen. Defining the field and identifying a research agenda: Business analytics.","DOI":"10.1016\/j.ejor.2019.10.001"},{"issue":"4","key":"5660_CR30","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1111\/deci.12165","volume":"46","author":"R Ishfaq","year":"2015","unstructured":"Ishfaq, R., & Raja, U. (2015). Bridging the healthcare access divide: a strategic planning model for rural telemedicine network. Decision Sciences, 46(4), 755\u2013790.","journal-title":"Decision Sciences"},{"key":"5660_CR31","doi-asserted-by":"crossref","unstructured":"Ji, Menglei, Jinlin Li, & Chun Peng. (2020). Two-stage chance-constrained telemedicine assignment model with no-show behavior and uncertain service duration. INFORMS International Conference on Service Science. Springer, 431\u2013442.","DOI":"10.1007\/978-3-030-75166-1_32"},{"key":"5660_CR32","doi-asserted-by":"crossref","unstructured":"Ji, Menglei, Shanshan Wang, Chun Peng, & Jinlin Li. (2022). Two-stage robust telemedicine assignment problem with uncertain service duration and no-show behaviours. Computers & Industrial Engineering 108226.","DOI":"10.1016\/j.cie.2022.108226"},{"issue":"1","key":"5660_CR33","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1287\/msom.2019.0844","volume":"22","author":"L Plambeck","year":"2020","unstructured":"Plambeck, L., & Erica, K. R. (2020). Alleviating poverty by empowering women through business model innovation: Manufacturing & service operations management insights and opportunities. Manufacturing & Service Operations Management, 22(1), 123\u2013134.","journal-title":"Manufacturing & Service Operations Management"},{"issue":"2","key":"5660_CR34","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1002\/cpt.2266","volume":"110","author":"L L\u00e9tinier","year":"2021","unstructured":"L\u00e9tinier, L., Jouganous, J., Benkebil, M., Bel-L\u00e9toile, A., Goehrs, C., Singier, A., Rouby, F., Lacroix, C., Miremont, G., Micallef, J., et al. (2021). Artificial intelligence for unstructured healthcare data: application to coding of patient reporting of adverse drug reactions. Clinical Pharmacology & Therapeutics, 110(2), 392\u2013400.","journal-title":"Clinical Pharmacology & Therapeutics"},{"issue":"1","key":"5660_CR35","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10479-016-2393-z","volume":"270","author":"MM Malik","year":"2018","unstructured":"Malik, M. M., Abdallah, S., & Ala\u2019raj, M. (2018). Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review. Annals of Operations Research, 270(1), 287\u2013312.","journal-title":"Annals of Operations Research"},{"key":"5660_CR36","doi-asserted-by":"crossref","unstructured":"Martinez, Oscar, Martinez, Carol, Parra, Carlos A., & Saul Rugeles, & Daniel R Suarez. (2021). Machine learning for surgical time prediction. Computer Methods and Programs in Biomedicine,208, 106220.","DOI":"10.1016\/j.cmpb.2021.106220"},{"key":"5660_CR37","unstructured":"Miner, Gary, John Elder\u00a0IV, Andrew Fast, Thomas Hill, Robert Nisbet, & Dursun Delen. (2012). Practical text mining and statistical analysis for non-structured text data applications. Academic Press."},{"key":"5660_CR38","unstructured":"National Health Commission of China. 2022. (2021) statistical bulletin on the development of china\u2019s health and wellness. http:\/\/www.gov.cn\/xinwen\/2022-07\/12\/content_5700670.htm."},{"key":"5660_CR39","doi-asserted-by":"crossref","unstructured":"Nguyen, Thu-Ba T., Iyer, Appa, & Sivakumar, & Stephen C Graves. (2017). Scheduling rules to achieve lead-time targets in outpatient appointment systems. Health Care Management Science,20(4), 578\u2013589.","DOI":"10.1007\/s10729-016-9374-2"},{"issue":"6","key":"5660_CR40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0234908","volume":"15","author":"CJ Ong","year":"2020","unstructured":"Ong, C. J., Orfanoudaki, A., Zhang, R., Francois Pierre, M., Caprasse, M. H., Ma, L., Fard, D., Balogun, O., Miller, M. I., Minnig, M., et al. (2020). Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports. PloS one, 15(6), e0234908.","journal-title":"PloS one"},{"key":"5660_CR41","doi-asserted-by":"crossref","unstructured":"Rahimi, Iman, & Amir H Gandomi. (2021). A comprehensive review and analysis of operating room and surgery scheduling. Archives of Computational Methods in Engineering,28(3), 1667\u20131688.","DOI":"10.1007\/s11831-020-09432-2"},{"key":"5660_CR42","doi-asserted-by":"crossref","unstructured":"Roshanaei, Vahid, Luong, Curtiss, Dionne, M., & Aleman, & David Urbach. (2017). Propagating logic-based benders\u2019 decomposition approaches for distributed operating room scheduling. European Journal of Operational Research,257(2), 439\u2013455.","DOI":"10.1016\/j.ejor.2016.08.024"},{"key":"5660_CR43","doi-asserted-by":"crossref","unstructured":"Saghafian, Soroush, Hopp, Wallace J., Iravani, Seyed MR., & Yao Cheng, & Daniel Diermeier. (2018). Workload management in telemedical physician triage and other knowledge-based service systems. Management Science,64(11), 5180\u20135197.","DOI":"10.1287\/mnsc.2017.2905"},{"key":"5660_CR44","doi-asserted-by":"crossref","unstructured":"Salah, Haya, & Sharan Srinivas. (2022). Predict, then schedule: Prescriptive analytics approach for machine learning-enabled sequential clinical scheduling. Computers & Industrial Engineering 108270.","DOI":"10.1016\/j.cie.2022.108270"},{"key":"5660_CR45","doi-asserted-by":"crossref","unstructured":"Song, Guopeng, & Daniel Kowalczyk, & Roel Leus. (2018). The robust machine availability problem-bin packing under uncertainty. IISE Transactions,50(11), 997\u20131012.","DOI":"10.1080\/24725854.2018.1468122"},{"key":"5660_CR46","doi-asserted-by":"crossref","unstructured":"Sunar, Nur, & Jayashankar\u00a0M Swaminathan. (2022). Socially relevant and inclusive operations management. Production and Operations Management .","DOI":"10.1111\/poms.13873"},{"key":"5660_CR47","doi-asserted-by":"crossref","unstructured":"Veerashetty, Sachinkumar, & Nagaraj B Patil. (2021). Manhattan distance-based histogram of oriented gradients for content-based medical image retrieval. International Journal of Computers and Applications,43(9), 924\u2013930.","DOI":"10.1080\/1206212X.2019.1653011"},{"key":"5660_CR48","doi-asserted-by":"crossref","unstructured":"Wang, Shanshan, & Jinlin Li, & Sanjay Mehrotra. (2021). Chance-constrained multiple bin packing problem with an application to operating room planning. INFORMS Journal on Computing,33(4), 1661\u20131677.","DOI":"10.1287\/ijoc.2020.1010"},{"key":"5660_CR49","unstructured":"Wiberg, & Holly\u00a0Mika. (2022). Data-driven healthcare via constraint learning and analytics. Ph.D. thesis, Massachusetts Institute of Technology."},{"key":"5660_CR50","doi-asserted-by":"crossref","unstructured":"Wong, Sebastien\u00a0C, Adam Gatt, Victor Stamatescu, & Mark\u00a0D McDonnell. (2016). Understanding data augmentation for classification: when to warp? 2016 international conference on digital image computing: techniques and applications (DICTA). IEEE, 1\u20136.","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"5660_CR51","volume-title":"Introduction to telemedicine","author":"R Wootton","year":"2017","unstructured":"Wootton, R., Craig, J., & Patterson, V. (2017). Introduction to telemedicine. CRC Press."},{"key":"5660_CR52","unstructured":"World Health Organization. (2020). Covid-19 significantly impacts health services for noncommunicable diseases. https:\/\/www.who.int\/news\/item\/01-06-2020-covid-19-significantly-impacts-health-services-for-noncommunicable-diseases."},{"issue":"1","key":"5660_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12913-018-3461-7","volume":"18","author":"C Zhang","year":"2018","unstructured":"Zhang, C., & Liu, Y. (2018). The salary of physicians in chinese public tertiary hospitals: a national cross-sectional and follow-up study. BMC health services research, 18(1), 1\u20139.","journal-title":"BMC health services research"},{"key":"5660_CR54","unstructured":"Zhang, Congle, Tyler Baldwin, Howard Ho, Benny Kimelfeld, & Yunyao Li. (2013). Adaptive parser-centric text normalization. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1159\u20131168."},{"issue":"1","key":"5660_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-020-01297-6","volume":"20","author":"D Zhang","year":"2020","unstructured":"Zhang, D., Yin, C., Zeng, J., Yuan, X., & Zhang, P. (2020). Combining structured and unstructured data for predictive models: a deep learning approach. BMC medical informatics and decision making, 20(1), 1\u201311.","journal-title":"BMC medical informatics and decision making"},{"issue":"1","key":"5660_CR56","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.ejor.2021.12.020","volume":"304","author":"C Zhou","year":"2023","unstructured":"Zhou, C., Hao, Y., Lan, Y., & Li, W. (2023). To introduce or not? strategic analysis of hospital operations with telemedicine. European journal of operational research, 304(1), 292\u2013307.","journal-title":"European journal of operational research"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-023-05660-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-023-05660-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-023-05660-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T11:20:27Z","timestamp":1727695227000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-023-05660-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,17]]},"references-count":56,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["5660"],"URL":"https:\/\/doi.org\/10.1007\/s10479-023-05660-4","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"type":"print","value":"0254-5330"},{"type":"electronic","value":"1572-9338"}],"subject":[],"published":{"date-parts":[[2023,11,17]]},"assertion":[{"value":"1 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}