{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:49:53Z","timestamp":1782002993836,"version":"3.54.5"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031064166","type":"print"},{"value":"9783031064173","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-06417-3_63","type":"book-chapter","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T06:03:16Z","timestamp":1655791396000},"page":"468-473","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Concept for Supporting Occupational Safety Risk Analysis with a Machine Learning Tool"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6940-376X","authenticated-orcid":false,"given":"Martin","family":"Westhoven","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Adolph","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"63_CR1","unstructured":"Mattei, P.-A., Frellsen, J.: MIWAE: deep generative modelling and imputation of incomplete data sets. In: International Conference on Machine Learning. PMLR (2019)"},{"issue":"2","key":"63_CR2","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3390\/genes10020087","volume":"10","author":"B Mirza","year":"2019","unstructured":"Mirza, B., et al.: Machine learning and integrative analysis of biomedical big data. Genes 10(2), 87 (2019)","journal-title":"Genes"},{"issue":"2","key":"63_CR3","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.1539-6924.1999.tb00399.x","volume":"19","author":"RT Clemen","year":"1999","unstructured":"Clemen, R.T., Winkler, R.L.: Combining probability distributions from experts in risk analysis. Risk Anal. 19(2), 187\u2013203 (1999)","journal-title":"Risk Anal."},{"issue":"3","key":"63_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang, Y., et al.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (CSUR) 53(3), 1\u201334 (2020)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"5","key":"63_CR5","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s12599-019-00595-2","volume":"61","author":"D Dellermann","year":"2019","unstructured":"Dellermann, D., et al.: Hybrid intelligence. Bus. Inf. Syst. Eng. 61(5), 637\u2013643 (2019)","journal-title":"Bus. Inf. Syst. Eng."},{"issue":"5","key":"63_CR6","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1111\/risa.12899","volume":"38","author":"T Aven","year":"2018","unstructured":"Aven, T.: An emerging new risk analysis science: foundations and implications. Risk Anal. 38(5), 876\u2013888 (2018)","journal-title":"Risk Anal."},{"issue":"5","key":"63_CR7","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1093\/occmed\/kqh074","volume":"54","author":"J Joy","year":"2004","unstructured":"Joy, J.: Occupational safety risk management in Australian mining. Occup. Med. 54(5), 311\u2013315 (2004)","journal-title":"Occup. Med."},{"key":"63_CR8","unstructured":"O\u2019Beirne, T., Napper, A.: Introduction of systematic safety assessment techniques to underground coal industry (1990)"},{"issue":"4","key":"63_CR9","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/S0950-4230(02)00008-6","volume":"15","author":"J Tixier","year":"2002","unstructured":"Tixier, J., et al.: Review of 62 risk analysis methodologies of industrial plants. J. Loss Prev. Process Ind. 15(4), 291\u2013303 (2002)","journal-title":"J. Loss Prev. Process Ind."},{"key":"63_CR10","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.psep.2019.04.015","volume":"126","author":"MT Amin","year":"2019","unstructured":"Amin, M.T., Khan, F., Amyotte, P.: A bibliometric review of process safety and risk analysis. Process Saf. Environ. Prot. 126, 366\u2013381 (2019)","journal-title":"Process Saf. Environ. Prot."},{"key":"63_CR11","doi-asserted-by":"crossref","unstructured":"De Silva, N., Ranasinghe, M., De Silva, C.R.: Risk analysis in maintainability of high-rise buildings under tropical conditions using ensemble neural network. Facilities 34( 1\/2),\u00a0 2\u201327 (2016).\u00a0https:\/\/doi.org\/10.1108\/F-05-2014-0047","DOI":"10.1108\/F-05-2014-0047"},{"key":"63_CR12","unstructured":"Kittelmann, M., Adolph, L., Michel, A., Packroff, R., Sch\u00fctte, M., Sommer, S. (Hrsg.): Handbuch Gef\u00e4hrdungsbeurteilung, 1st edn. Bundesanstalt f\u00fcr Arbeitsschutz und Arbeitsmedizin, Dortmund (2022)"},{"key":"63_CR13","unstructured":"BAuA. Handbuch Gef\u00e4hrdungsbeurteilung Teil 2: Gef\u00e4hrdungsfaktoren (2021). https:\/\/www.baua.de\/DE\/Themen\/Arbeitsgestaltung-im-Betrieb\/Gefaehrdungsbeurteilung\/Expertenwissen\/Expertenwissen_node.html. Accessed 17 Oct 2021"},{"issue":"6","key":"63_CR14","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1080\/1359432X.2017.1380626","volume":"26","author":"MF Dollard","year":"2017","unstructured":"Dollard, M.F., et al.: Psychosocial safety climate (PSC) and enacted PSC for workplace bullying and psychological health problem reduction. Eur. J. Work Organ. Psy. 26(6), 844\u2013857 (2017)","journal-title":"Eur. J. Work Organ. Psy."},{"key":"63_CR15","unstructured":"Shams, R.: Semi-supervised classification for natural language processing. arXiv preprint arXiv:1409.7612 (2014)"},{"key":"63_CR16","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"63_CR17","unstructured":"Doran, D., Schulz, S., Besold, T.R.: What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794 (2017)"},{"issue":"5","key":"63_CR18","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/3121357","volume":"24","author":"A Schmidt","year":"2017","unstructured":"Schmidt, A., Herrmann, T.: Intervention user interfaces: a new interaction paradigm for automated systems. Interactions 24(5), 40\u201345 (2017)","journal-title":"Interactions"},{"key":"63_CR19","unstructured":"Shneiderman, B., Plaisant, C.: Designing the user Interface: Strategies for Effective Human-Computer Interaction. Pearson Education India (2010)"},{"key":"63_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1007\/978-3-030-50334-5_20","volume-title":"Artificial Intelligence in HCI","author":"T Herrmann","year":"2020","unstructured":"Herrmann, T.: Socio-technical design of hybrid intelligence systems \u2013 the case of predictive maintenance. In: Degen, H., Reinerman-Jones, L. (eds.) HCII 2020. LNCS, vol. 12217, pp. 298\u2013309. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-50334-5_20"}],"container-title":["Communications in Computer and Information Science","HCI International 2022 Posters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-06417-3_63","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T00:06:17Z","timestamp":1782000377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-06417-3_63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031064166","9783031064173"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-06417-3_63","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.hci.international\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}