{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T07:12:19Z","timestamp":1783753939939,"version":"3.55.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032283153","type":"print"},{"value":"9783032283160","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-28316-0_20","type":"book-chapter","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:29:06Z","timestamp":1780342146000},"page":"351-369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Designing a Deployable ML Development Framework that Operationalizes Trustworthy Predictive Applications in the Medical Field"],"prefix":"10.1007","author":[{"given":"Arin","family":"Brahma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kala","family":"Seal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasaman","family":"Ghasemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir","family":"Chatterjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Kompa, B., Snoek, J., Beam, A.L.: Second opinion needed: communicating uncertainty in medical machine learning. npj Digit. Med. 4(1), 4 (2021)","DOI":"10.1038\/s41746-020-00367-3"},{"issue":"11","key":"20_CR2","doi-asserted-by":"publisher","first-page":"5088","DOI":"10.3390\/app11115088","volume":"11","author":"AM Antoniadi","year":"2021","unstructured":"Antoniadi, A.M., et al.: Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl. Sci. 11(11), 5088 (2021). https:\/\/doi.org\/10.3390\/app11115088","journal-title":"Appl. Sci."},{"issue":"10","key":"20_CR3","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1136\/medethics-2021-107529","volume":"48","author":"JJ Wadden","year":"2022","unstructured":"Wadden, J.J.: Defining the undefinable: the black box problem in healthcare artificial intelligence. J. Med. Ethics 48(10), 764\u2013768 (2022)","journal-title":"J. Med. Ethics"},{"issue":"4","key":"20_CR4","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1109\/TETC.2022.3171314","volume":"10","author":"Y Jia","year":"2022","unstructured":"Jia, Y., McDermid, J., Lawton, T., Habli, I.: The role of explainability in assuring safety of machine learning in healthcare. IEEE Trans. Emerg. Top. Comput. 10(4), 1746\u20131760 (2022)","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"20_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104510","volume":"153","author":"F Cabitza","year":"2021","unstructured":"Cabitza, F., Campagner, A.: The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int. J. Med. Informatics 153, 104510 (2021). https:\/\/doi.org\/10.1016\/j.ijmedinf.2021.104510","journal-title":"Int. J. Med. Informatics"},{"issue":"1","key":"20_CR6","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.ejor.2014.06.034","volume":"240","author":"M Samorani","year":"2015","unstructured":"Samorani, M., LaGanga, L.R.: Outpatient appointment scheduling given individual day- dependent no-show predictions. Eur. J. Oper. Res. 240(1), 245\u2013257 (2015)","journal-title":"Eur. J. Oper. Res."},{"key":"20_CR7","doi-asserted-by":"publisher","first-page":"509","DOI":"10.2147\/RMHP.S232114","volume":"13","author":"D Marbouh","year":"2020","unstructured":"Marbouh, D., et al.: Evaluating the impact of patient no-shows on service quality. Risk Manag. Healthc. Policy 13, 509\u2013517 (2020)","journal-title":"Risk Manag. Healthc. Policy"},{"issue":"3","key":"20_CR8","doi-asserted-by":"publisher","first-page":"836","DOI":"10.4338\/ACI-2014-04-RA-0026","volume":"5","author":"Y Huang","year":"2014","unstructured":"Huang, Y., Hanauer, D.A.: Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl. Clin. Inform. 5(3), 836\u2013860 (2014)","journal-title":"Appl. Clin. Inform."},{"issue":"4","key":"20_CR9","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1177\/2150131913498513","volume":"4","author":"E Kaplan-Lewis","year":"2013","unstructured":"Kaplan-Lewis, E., Percac-Lima, S.: No-show to primary care appointments: why patients do not come. J. Prim. Care Community Health 4(4), 251\u2013255 (2013)","journal-title":"J. Prim. Care Community Health"},{"issue":"11","key":"20_CR10","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3390\/info13110507","volume":"13","author":"LHA Salazar","year":"2022","unstructured":"Salazar, L.H.A., Parreira, W.D., Fernandes, A.M.D.R., Leithardt, V.R.Q.: No-show in medical appointments with machine learning techniques: a systematic literature review. Information 13(11), 507 (2022). https:\/\/doi.org\/10.3390\/info13110507","journal-title":"Information"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Almeida, R., Silva, N. A., and Vasconcelos, A.: A machine learning approach for real time prediction of last minute medical appointments no-shows. In: Proceedings of the 14th International Conference on Health Informatics (HEALTHINF), pp. 328\u2013336. SCITEPRESS (2021)","DOI":"10.5220\/0010221903280336"},{"key":"20_CR12","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"20_CR13","doi-asserted-by":"publisher","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: Proceedings of the First International Workshop on Multiple Classifier Systems, pp. 1\u201315. Springer, Cham (2000). https:\/\/doi.org\/10.1007\/3-540-45014-9_1","DOI":"10.1007\/3-540-45014-9_1"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Alizadehsani, R., Roshanzamir, M., Hussain, S., et al.: Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991\u20132020). Ann. Oper. Res. 1\u201342. (2021)","DOI":"10.1007\/s10479-021-04006-2"},{"issue":"1","key":"20_CR15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"issue":"5","key":"20_CR16","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: A gradient boosting machine. Ann. Stat. 29(5), 1189\u20131232 (2001). https:\/\/doi.org\/10.1214\/aos\/1013203451","journal-title":"Ann. Stat."},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., Sturdivant, R.X.: Applied logistic regression (3rd ed.). Wiley (2013)","DOI":"10.1002\/9781118548387"},{"key":"20_CR18","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N.D. (eds.): Dataset shift in machine learning. MIT Press (2009). https:\/\/mitpress.mit.edu\/9780262545877\/dataset-shift-in-machine-learning\/","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"20_CR20","unstructured":"Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), Vol. 2, pp. 1137\u20131143 (1995). https:\/\/www.ijcai.org\/Proceedings\/95-2\/Papers\/016.pdf"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Efron, B., Tibshirani, R.J.: An introduction to the bootstrap. Chapman & Hall\/CRC. (1993)","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"20_CR22","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), pp. 4765\u20134774 (2017)"},{"issue":"3","key":"20_CR23","doi-asserted-by":"publisher","first-page":"281","DOI":"10.4236\/ojs.2012.23034","volume":"2","author":"M Tsao","year":"2012","unstructured":"Tsao, M., Ling, X.: Subsampling method for robust estimation of regression models. Open J. Stat. 2(3), 281\u2013296 (2012). https:\/\/doi.org\/10.4236\/ojs.2012.23034","journal-title":"Open J. Stat."},{"key":"20_CR24","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on Machine Learning (ICML), pp. 1050\u20131059. PMLR (2016)"},{"key":"20_CR25","unstructured":"Zhang, H.: The optimality of Naive Bayes. AA 1(2), 3 (2004)"},{"key":"20_CR26","doi-asserted-by":"publisher","unstructured":"Alshammari, R., Alwan, M., Al-Dhubiani, H.: The prediction of outpatient no-show visits using deep neural networks. Int. J. Adv. Comput. Sci. Appl. 11(10), 509\u2013515 (2020). https:\/\/doi.org\/10.14569\/IJACSA.2020.0111066","DOI":"10.14569\/IJACSA.2020.0111066"},{"key":"20_CR27","doi-asserted-by":"publisher","unstructured":"Toffaha, K.M., Simsekler, M.C.E., Omar, M.A., ElKebbi, I.: Predicting patient no-shows using machine learning: a comprehensive review and future research agenda. Intell.-Based Med. 100229 (2025). https:\/\/doi.org\/10.1016\/j.ibmed.2025.100229","DOI":"10.1016\/j.ibmed.2025.100229"},{"issue":"9","key":"20_CR28","doi-asserted-by":"publisher","first-page":"e489","DOI":"10.1016\/S2589-7500(20)30186-2","volume":"2","author":"J Futoma","year":"2020","unstructured":"Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., Celi, L.A.: The myth of generalisability in clinical research and machine learning in health care. Lancet Digit. Health 2(9), e489\u2013e492 (2020). https:\/\/doi.org\/10.1016\/S2589-7500(20)30186-2","journal-title":"Lancet Digit. Health"},{"key":"20_CR29","doi-asserted-by":"publisher","unstructured":"McDermott, M.B.A., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., Ghassemi, M.: Reproducibility in machine learning for health research: still a ways to go. Sci. Transl. Med. 13(586), eabb1655 (2021). https:\/\/doi.org\/10.1126\/scitranslmed.abb1655","DOI":"10.1126\/scitranslmed.abb1655"},{"key":"20_CR30","doi-asserted-by":"publisher","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. ACM (2016). https:\/\/doi.org\/10.1145\/2939672.2939778","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"20_CR31","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1038\/s42256-018-0004-1","volume":"1","author":"E Begoli","year":"2019","unstructured":"Begoli, E., Bhattacharya, T., Kusnezov, D.: The need for uncertainty quantification in machine-assisted medical decision making. Nat. Mach. Intell. 1(1), 20\u201323 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"20_CR32","doi-asserted-by":"publisher","unstructured":"Brahma, A., and Dosher, M.: Patient no-show prediction uncertainty modeling: Compressed data and code . Mendeley Data, V1 (2025). https:\/\/doi.org\/10.17632\/mcwpryrs4k.1","DOI":"10.17632\/mcwpryrs4k.1"},{"issue":"3","key":"20_CR33","doi-asserted-by":"publisher","first-page":"45","DOI":"10.2753\/MIS0742-1222240302","volume":"24","author":"K Peffers","year":"2007","unstructured":"Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manag. Inf. Syst. 24(3), 45\u201377 (2007)","journal-title":"J. Manag. Inf. Syst."}],"container-title":["Lecture Notes in Computer Science","Design for Better Futures: Beyond the Science of the Artificial. Completed Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-28316-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T06:20:05Z","timestamp":1783750805000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-28316-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032283153","9783032283160"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-28316-0_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DESRIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Design Science Research in Information Systems and Technology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"M\u00fcnster","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"desrist2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/desrist2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}