{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:15:15Z","timestamp":1766733315294,"version":"3.37.3"},"reference-count":45,"publisher":"Walter de Gruyter GmbH","issue":"5-6","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["769807"],"award-info":[{"award-number":["769807"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Providing a suitable rehabilitation at home after an acute episode or a chronic disease is a major issue as it helps people to live independently and enhance their quality of life. However, as the rehabilitation period usually lasts some months, the continuity of care is often interrupted in the transition from the hospital to the home. Relieving the healthcare system and personalizing the care or even bringing care to the patients\u2019 home to a greater extent is, in consequence, the superior need. This is why we propose to make use of information technology to come to participatory design driven by users needs and the personalisation of the care pathways enabled by technology. To allow this, patient rehabilitation at home needs to be supported by automatic decision-making, as physicians cannot constantly supervise the rehabilitation process. Thus, we need computer-assisted patient rehabilitation, which monitors the fitness of the current patient plan to detect sub-optimality, proposes personalised changes for a patient and eventually generalizes over patients and proposes better initial plans. Therefore, we will explain the use case of patient rehabilitation at home, the basic challenges in this field and machine learning applications that could address these challenges by technical means.<\/jats:p>","DOI":"10.1515\/itit-2019-0022","type":"journal-article","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T09:03:13Z","timestamp":1574845393000},"page":"273-284","source":"Crossref","is-referenced-by-count":5,"title":["Continuous support for rehabilitation using machine learning"],"prefix":"10.1515","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5831-1018","authenticated-orcid":false,"given":"Patrick","family":"Philipp","sequence":"first","affiliation":[{"name":"366375 Forschungszentrum Informatik , Information Process Engineering , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"Merkle","sequence":"additional","affiliation":[{"name":"366375 Forschungszentrum Informatik , Information Process Engineering , Karlsruhe , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Gand","sequence":"additional","affiliation":[{"name":"TU Dresden , Chair of Wirtschaftsinformatik, esp. Systems Development , Dresden , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carola","family":"Gi\u00dfke","sequence":"additional","affiliation":[{"name":"TU Dresden , Chair of Wirtschaftsinformatik, esp. Systems Development , Dresden , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"2023033120044232379_j_itit-2019-0022_ref_001_w2aab3b7c14b1b6b1ab2ab1Aa","unstructured":"Bengio, Y., Ducharme, R., Vincent, P., and Janvin, C. A neural probabilistic language model. Journal of Machine Learning Research 3 (2003), 1137\u20131155."},{"key":"2023033120044232379_j_itit-2019-0022_ref_002_w2aab3b7c14b1b6b1ab2ab2Aa","doi-asserted-by":"crossref","unstructured":"Berners-Lee, T., Hendler, J., Lassila, O., et al. The semantic web. Scientific American 284, 5 (2001), 28\u201337.","DOI":"10.1038\/scientificamerican0501-34"},{"key":"2023033120044232379_j_itit-2019-0022_ref_003_w2aab3b7c14b1b6b1ab2ab3Aa","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., and Yakhnenko, O. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States. (2013), pp.\u20092787\u20132795."},{"key":"2023033120044232379_j_itit-2019-0022_ref_004_w2aab3b7c14b1b6b1ab2ab4Aa","doi-asserted-by":"crossref","unstructured":"Chaiyawat, P., Kulkantrakorn, K., and Sritipsukho, P. Effectiveness of home rehabilitation for ischemic stroke. Neurology international 1, 1 (2009).","DOI":"10.4081\/ni.2009.e10"},{"key":"2023033120044232379_j_itit-2019-0022_ref_005_w2aab3b7c14b1b6b1ab2ab5Aa","doi-asserted-by":"crossref","unstructured":"Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., Morton, S.\u2009C., and Shekelle, P.\u2009G. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of internal medicine 144, 10 (2006), 742\u2013752.","DOI":"10.7326\/0003-4819-144-10-200605160-00125"},{"key":"2023033120044232379_j_itit-2019-0022_ref_006_w2aab3b7c14b1b6b1ab2ab6Aa","unstructured":"Chen, J., Song, L., Wainwright, M.\u2009J., and Jordan, M.\u2009I. Learning to explain: An information-theoretic perspective on model interpretation. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10-15, 2018 (2018), pp.\u2009882\u2013891."},{"key":"2023033120044232379_j_itit-2019-0022_ref_007_w2aab3b7c14b1b6b1ab2ab7Aa","unstructured":"Chu, W., Li, L., Reyzin, L., and Schapire, R.\u2009E. Contextual bandits with linear payoff functions. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011 (2011), pp.\u2009208\u2013214."},{"key":"2023033120044232379_j_itit-2019-0022_ref_008_w2aab3b7c14b1b6b1ab2ab8Aa","unstructured":"Clifford, J. The UN disability convention and its impact on European equality law. The Equal Rights Review 6 (2011), 11\u201325."},{"key":"2023033120044232379_j_itit-2019-0022_ref_009_w2aab3b7c14b1b6b1ab2ab9Aa","unstructured":"Curry, N., and Ham, C. Clinical and service integration: the route to improved outcomes. The King\u2019s Fund (2020)."},{"key":"2023033120044232379_j_itit-2019-0022_ref_010_w2aab3b7c14b1b6b1ab2ac10Aa","doi-asserted-by":"crossref","unstructured":"Doll, B.\u2009B., Simon, D.\u2009A., and Daw, N.\u2009D. The ubiquity of model-based reinforcement learning. Current opinion in neurobiology 22, 6 (2012), 1075\u20131081.","DOI":"10.1016\/j.conb.2012.08.003"},{"key":"2023033120044232379_j_itit-2019-0022_ref_011_w2aab3b7c14b1b6b1ab2ac11Aa","unstructured":"Ester, M., Kriegel, H., Sander, J., and Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, USA (1996), pp.\u2009226\u2013231."},{"key":"2023033120044232379_j_itit-2019-0022_ref_012_w2aab3b7c14b1b6b1ab2ac12Aa","unstructured":"Forgey, E. Cluster analysis of multivariate data: Efficiency vs. interpretability of classification. Biometrics 21, 3 (1965), 768\u2013769."},{"key":"2023033120044232379_j_itit-2019-0022_ref_013_w2aab3b7c14b1b6b1ab2ac13Aa","unstructured":"Gilmer, J., Schoenholz, S.\u2009S., Riley, P.\u2009F., Vinyals, O., and Dahl, G.\u2009E. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 (2017), pp.\u20091263\u20131272."},{"key":"2023033120044232379_j_itit-2019-0022_ref_014_w2aab3b7c14b1b6b1ab2ac14Aa","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13\u201317, 2016 (2016), pp.\u2009855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"key":"2023033120044232379_j_itit-2019-0022_ref_015_w2aab3b7c14b1b6b1ab2ac15Aa","unstructured":"Ho, T.\u2009K., Random decision forests. In Third International Conference on Document Analysis and Recognition, ICDAR 1995, August 14\u201315, 1995, Montreal, Canada. Volume I (1995), pp.\u2009278\u2013282."},{"key":"2023033120044232379_j_itit-2019-0022_ref_016_w2aab3b7c14b1b6b1ab2ac16Aa","unstructured":"Jensen, F.\u2009V., et al. An introduction to Bayesian networks, vol.\u2009210. UCL press London, 1996."},{"key":"2023033120044232379_j_itit-2019-0022_ref_017_w2aab3b7c14b1b6b1ab2ac17Aa","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., and Zhao, J. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, July 26\u201331, 2015, Beijing, China, Volume 1: Long Papers (2015), pp.\u2009687\u2013696.","DOI":"10.3115\/v1\/P15-1067"},{"key":"2023033120044232379_j_itit-2019-0022_ref_018_w2aab3b7c14b1b6b1ab2ac18Aa","doi-asserted-by":"crossref","unstructured":"Kimmig, A., Mihalkova, L., and Getoor, L. Lifted graphical models: a survey. Machine Learning 99, 1 (2015), 1\u201345.","DOI":"10.1007\/s10994-014-5443-2"},{"key":"2023033120044232379_j_itit-2019-0022_ref_019_w2aab3b7c14b1b6b1ab2ac19Aa","doi-asserted-by":"crossref","unstructured":"Kinsman, L., Rotter, T., James, E., Snow, P., and Willis, J. What is a clinical pathway? development of a definition to inform the debate. BMC medicine 8, 1 (2010), 31.","DOI":"10.1186\/1741-7015-8-31"},{"key":"2023033120044232379_j_itit-2019-0022_ref_020_w2aab3b7c14b1b6b1ab2ac20Aa","doi-asserted-by":"crossref","unstructured":"Krening, S., Harrison, B., Feigh, K.\u2009M., Jr., Isbell, C.\u2009L., Riedl, M., Thomaz, A. Learning from explanations using sentiment and advice in RL. IEEE Trans. Cognitive and Developmental Systems 9, 1 (2017), 44\u201355.","DOI":"10.1109\/TCDS.2016.2628365"},{"key":"2023033120044232379_j_itit-2019-0022_ref_021_w2aab3b7c14b1b6b1ab2ac21Aa","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bengio, Y., and Hinton, G.\u2009E. Deep learning. Nature 521, 7553 (2015), 436\u2013444.","DOI":"10.1038\/nature14539"},{"key":"2023033120044232379_j_itit-2019-0022_ref_022_w2aab3b7c14b1b6b1ab2ac22Aa","unstructured":"Liu, E.\u2009Z., Guu, K., Pasupat, P., Shi, T., and Liang, P. Reinforcement learning on web interfaces using workflow-guided exploration. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings (2018)."},{"key":"2023033120044232379_j_itit-2019-0022_ref_023_w2aab3b7c14b1b6b1ab2ac23Aa","doi-asserted-by":"crossref","unstructured":"Liu, R., Srinivasan, R.\u2009V., Zolfaghar, K., Chin, S., Roy, S.\u2009B., Hasan, A., and Hazel, D. Pathway-finder: An interactive recommender system for supporting personalized care pathways. In 2014 IEEE International Conference on Data Mining Workshops, ICDM Workshops 2014, Shenzhen, China, December 14, 2014 (2014), pp.\u20091219\u20131222.","DOI":"10.1109\/ICDMW.2014.37"},{"key":"2023033120044232379_j_itit-2019-0022_ref_024_w2aab3b7c14b1b6b1ab2ac24Aa","doi-asserted-by":"crossref","unstructured":"Lloyd, S.\u2009P. Least squares quantization in PCM. IEEE Trans. Information Theory 28, 2 (1982), 129\u2013136.","DOI":"10.1109\/TIT.1982.1056489"},{"key":"2023033120044232379_j_itit-2019-0022_ref_025_w2aab3b7c14b1b6b1ab2ac25Aa","unstructured":"Mansell, J., Knapp, M., Beadle-Brown, J., and Beecham, J. Deinstitutionalisation and community living\u2013outcomes and costs: report of a European Study. Volume 2: Main Report. University of Kent, 2007."},{"key":"2023033120044232379_j_itit-2019-0022_ref_026_w2aab3b7c14b1b6b1ab2ac26Aa","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.\u2009S., and Dean, J. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5\u20138, 2013, Lake Tahoe, Nevada, United States. (2013), pp.\u20093111\u20133119."},{"key":"2023033120044232379_j_itit-2019-0022_ref_027_w2aab3b7c14b1b6b1ab2ac27Aa","doi-asserted-by":"crossref","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.\u2009A., Veness, J., Bellemare, M.\u2009G., Graves, A., Riedmiller, M.\u2009A., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529\u2013533.","DOI":"10.1038\/nature14236"},{"key":"2023033120044232379_j_itit-2019-0022_ref_028_w2aab3b7c14b1b6b1ab2ac28Aa","doi-asserted-by":"crossref","unstructured":"Nachabe, L., Girod-Genet, M., and El Hassan, B. Unified data model for wireless sensor network. IEEE Sensors Journal 15, 7 (2015), 3657\u20133667.","DOI":"10.1109\/JSEN.2015.2393951"},{"key":"2023033120044232379_j_itit-2019-0022_ref_029_w2aab3b7c14b1b6b1ab2ac29Aa","unstructured":"Nickel, M., Tresp, V., and Kriegel, H. A three-way model for collective learning on multi-relational data. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28\u2013July 2, 2011 (2011), pp.\u2009809\u2013816."},{"key":"2023033120044232379_j_itit-2019-0022_ref_030_w2aab3b7c14b1b6b1ab2ac30Aa","unstructured":"Puterman, M.\u2009L. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, 2014."},{"key":"2023033120044232379_j_itit-2019-0022_ref_031_w2aab3b7c14b1b6b1ab2ac31Aa","unstructured":"Raghavan, H., Madani, O., and Jones, R. Active learning with feedback on features and instances. Journal of Machine Learning Research 7 (2006), 1655\u20131686."},{"key":"2023033120044232379_j_itit-2019-0022_ref_032_w2aab3b7c14b1b6b1ab2ac32Aa","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.\u2009T., Singh, S., and Guestrin, C. \u201cWhy should I trust you?\u201d: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13\u201317, 2016 (2016), pp.\u20091135\u20131144.","DOI":"10.1145\/2939672.2939778"},{"key":"2023033120044232379_j_itit-2019-0022_ref_033_w2aab3b7c14b1b6b1ab2ac33Aa","unstructured":"Ribeiro, M.\u2009T., Singh, S., and Guestrin, C. Anchors: High-precision model-agnostic explanations. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2\u20137, 2018 (2018), pp.\u20091527\u20131535."},{"key":"2023033120044232379_j_itit-2019-0022_ref_034_w2aab3b7c14b1b6b1ab2ac34Aa","unstructured":"Ross, S., and Bagnell, D. Efficient reductions for imitation learning. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, May 13\u201315, 2010 (2010), pp.\u2009661\u2013668."},{"key":"2023033120044232379_j_itit-2019-0022_ref_035_w2aab3b7c14b1b6b1ab2ac35Aa","doi-asserted-by":"crossref","unstructured":"Rozanov, Y.\u2009A. Markov random fields. In Markov Random Fields. Springer (1982), pp.\u200955\u2013102.","DOI":"10.1007\/978-1-4613-8190-7_2"},{"key":"2023033120044232379_j_itit-2019-0022_ref_036_w2aab3b7c14b1b6b1ab2ac36Aa","doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., and Smola, A.\u2009J. Learning with Kernels: support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning series. MIT Press, 2002.","DOI":"10.7551\/mitpress\/4175.001.0001"},{"key":"2023033120044232379_j_itit-2019-0022_ref_037_w2aab3b7c14b1b6b1ab2ac37Aa","unstructured":"Schulman, J., Levine, S., Abbeel, P., Jordan, M.\u2009I., and Moritz, P. Trust region policy optimization. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6\u201311 July 2015 (2015), pp.\u20091889\u20131897."},{"key":"2023033120044232379_j_itit-2019-0022_ref_038_w2aab3b7c14b1b6b1ab2ac38Aa","doi-asserted-by":"crossref","unstructured":"Stumpf, S., Rajaram, V., Li, L., Wong, W., Burnett, M.\u2009M., Dietterich, T.\u2009G., Sullivan, E., and Herlocker, J.\u2009L. Interacting meaningfully with machine learning systems: Three experiments. Int. J. Hum.-Comput. Stud. 67, 8 (2009), 639\u2013662.","DOI":"10.1016\/j.ijhcs.2009.03.004"},{"key":"2023033120044232379_j_itit-2019-0022_ref_039_w2aab3b7c14b1b6b1ab2ac39Aa","unstructured":"Sutton, R.\u2009S., and Barto, A.\u2009G. Reinforcement learning: An introduction. MIT press, 2018."},{"key":"2023033120044232379_j_itit-2019-0022_ref_040_w2aab3b7c14b1b6b1ab2ac40Aa","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.\u2009E., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. Going deeper with convolutions. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7\u201312, 2015 (2015), pp.\u20091\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2023033120044232379_j_itit-2019-0022_ref_041_w2aab3b7c14b1b6b1ab2ac41Aa","unstructured":"World Health Organization, et\u2009al. World report on disability 2011."},{"key":"2023033120044232379_j_itit-2019-0022_ref_042_w2aab3b7c14b1b6b1ab2ac42Aa","doi-asserted-by":"crossref","unstructured":"Yanardag, P., and Vishwanathan, S.\u2009V.\u2009N. Deep graph kernels. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10\u201313, 2015 (2015), pp.\u20091365\u20131374.","DOI":"10.1145\/2783258.2783417"},{"key":"2023033120044232379_j_itit-2019-0022_ref_043_w2aab3b7c14b1b6b1ab2ac43Aa","unstructured":"Zadrozny, B., Langford, J., and Abe, N. Cost-sensitive learning by cost-proportionate example weighting. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 19\u201322 December 2003, Melbourne, Florida, USA (2003), p.\u2009435."},{"key":"2023033120044232379_j_itit-2019-0022_ref_044_w2aab3b7c14b1b6b1ab2ac44Aa","doi-asserted-by":"crossref","unstructured":"Zhu, M., Cheng, L., Armstrong, J.\u2009J., Poss, J.\u2009W., Hirdes, J.\u2009P., and Stolee, P. Using machine learning to plan rehabilitation for home care clients: Beyond \u201cblack-box\u201d predictions. In Machine Learning in Healthcare Informatics. 2014, pp.\u2009181\u2013207.","DOI":"10.1007\/978-3-642-40017-9_9"},{"key":"2023033120044232379_j_itit-2019-0022_ref_045_w2aab3b7c14b1b6b1ab2ac45Aa","doi-asserted-by":"crossref","unstructured":"Zhu, M., Zhang, Z., Hirdes, J.\u2009P., and Stolee, P. Using machine learning algorithms to guide rehabilitation planning for home care clients. BMC Med. Inf. & Decision Making 7 (2007), 41.","DOI":"10.1186\/1472-6947-7-41"}],"container-title":["it - Information Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/itit.2019.61.issue-5-6\/itit-2019-0022\/itit-2019-0022.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2019-0022\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2019-0022\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T20:01:56Z","timestamp":1722110516000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/itit-2019-0022\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,1]]},"references-count":45,"journal-issue":{"issue":"5-6","published-online":{"date-parts":[[2019,11,22]]},"published-print":{"date-parts":[[2019,10,25]]}},"alternative-id":["10.1515\/itit-2019-0022"],"URL":"https:\/\/doi.org\/10.1515\/itit-2019-0022","relation":{},"ISSN":["2196-7032","1611-2776"],"issn-type":[{"type":"electronic","value":"2196-7032"},{"type":"print","value":"1611-2776"}],"subject":[],"published":{"date-parts":[[2019,10,1]]}}}