{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T15:13:22Z","timestamp":1768576402739,"version":"3.49.0"},"reference-count":95,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Fondazione IRCCS Istituto Nazionale dei Tumori","award":["101057048"],"award-info":[{"award-number":["101057048"]}]},{"DOI":"10.13039\/501100002954","name":"Universit\u00e0 degli Studi di Milano-Bicocca","doi-asserted-by":"publisher","award":["PNC0000003"],"award-info":[{"award-number":["PNC0000003"]}],"id":[{"id":"10.13039\/501100002954","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-025-02327-4","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:50:18Z","timestamp":1768531818000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Causal Discovery Workflow for Rare Diseases: Experts-in-the-Loop Analysis of Sparse Longitudinal Data"],"prefix":"10.1007","volume":"50","author":[{"given":"Niccol\u00f2","family":"Rocchi","sequence":"first","affiliation":[]},{"given":"Alessio","family":"Zanga","sequence":"additional","affiliation":[]},{"given":"Alice","family":"Bernasconi","sequence":"additional","affiliation":[]},{"given":"Alessandro","family":"Gronchi","sequence":"additional","affiliation":[]},{"given":"Dario","family":"Callegaro","sequence":"additional","affiliation":[]},{"given":"Alessandra","family":"Borghi","sequence":"additional","affiliation":[]},{"given":"Paolo Giovanni","family":"Casali","sequence":"additional","affiliation":[]},{"given":"Salvatore","family":"Provenzano","sequence":"additional","affiliation":[]},{"given":"Rosalba","family":"Miceli","sequence":"additional","affiliation":[]},{"given":"Annalisa","family":"Trama","sequence":"additional","affiliation":[]},{"given":"Fabio","family":"Stella","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"key":"2327_CR1","unstructured":"Alessandro B (2024) Structure learning and knowledge extraction with continuous time bayesian network. PhD thesis, https:\/\/hdl.handle.net\/20.500.14242\/161745"},{"key":"2327_CR2","doi-asserted-by":"publisher","unstructured":"Amirkhani H, Rahmati M, Lucas PJF, et\u00a0al (2017) Exploiting experts\u2019 knowledge for structure learning of bayesian networks. IEEE Trans Pattern Anal Mach Intell 39(11):2154\u20132170. https:\/\/doi.org\/10.1109\/tpami.2016.2636828","DOI":"10.1109\/tpami.2016.2636828"},{"key":"2327_CR3","doi-asserted-by":"publisher","unstructured":"Andrews B, Ramsey J, Cooper GF (2018) Scoring bayesian networks of mixed variables. International Journal of Data Science and Analytics 6(1):3\u201318.https:\/\/doi.org\/10.1007\/s41060-017-0085-7","DOI":"10.1007\/s41060-017-0085-7"},{"key":"2327_CR4","doi-asserted-by":"publisher","unstructured":"Angelopoulos N, Cussens J (2008) Bayesian learning of bayesian networks with informative priors. Ann Math Artif Intel 54(1\u20133):53\u201398.https:\/\/doi.org\/10.1007\/s10472-009-9133-x","DOI":"10.1007\/s10472-009-9133-x"},{"key":"2327_CR5","doi-asserted-by":"publisher","unstructured":"Bareinboim E, Correa JD, Ibeling D, et\u00a0al (2022) On Pearl\u2019s Hierarchy and the Foundations of Causal Inference, ACM, pp 507\u2013556.https:\/\/doi.org\/10.1145\/3501714.3501743","DOI":"10.1145\/3501714.3501743"},{"key":"2327_CR6","doi-asserted-by":"publisher","unstructured":"Berkson J (1946) Limitations of the application of fourfold table analysis to hospital data. Biometrics Bulletin 2(3):4https:\/\/doi.org\/10.2307\/3002000","DOI":"10.2307\/3002000"},{"key":"2327_CR7","unstructured":"Borboudakis G, Tsamardinos I (2012) Incorporating causal prior knowledge as path-constraints in bayesian networks and maximal ancestral graphs. In: Proceedings of the 29th International Coference on International Conference on Machine Learning. Omnipress, Madison, WI, USA, ICML\u201912, pp 427\u2013434"},{"key":"2327_CR8","unstructured":"Borboudakis G, Tsamardinos I (2013) Scoring and searching over bayesian networks with causal and associative priors. In: Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence. AUAI Press, Arlington, Virginia, USA, UAI\u201913, pp 102\u2013111"},{"key":"2327_CR9","unstructured":"Bouckaert RR (1995) Bayesian belief networks: from construction to inference. PhD thesis"},{"key":"2327_CR10","doi-asserted-by":"publisher","unstructured":"Bregoli A, Scutari M, Stella F (2021) A constraint-based algorithm for the structural learning of continuous-time bayesian networks. Int J Approx Reason 138:105\u201312https:\/\/doi.org\/10.1016\/j.ijar.2021.08.005, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0888613X21001304","DOI":"10.1016\/j.ijar.2021.08.005"},{"key":"2327_CR11","unstructured":"Brouillard P, Lachapelle S, Lacoste A, et\u00a0al (2020) Differentiable causal discovery from interventional data. In: Larochelle H, Ranzato M, Hadsell R, et\u00a0al (eds) Advances in Neural Information Processing Systems, vol\u00a033. Curran Associates, Inc., pp 21865\u201321877, https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/f8b7aa3a0d349d9562b424160ad18612-Paper.pdf"},{"key":"2327_CR12","unstructured":"Brouillard P, Taslakian P, Lacoste A, et\u00a0al (2022) Typing assumptions improve identification in causal discovery. In: Sch\u00f6lkopf B, Uhler C, Zhang K (eds) Proceedings of the First Conference on Causal Learning and Reasoning, Proceedings of Machine Learning Research, vol 177. arXiv, pp 162\u2013177, https:\/\/proceedings.mlr.press\/v177\/brouillard22a.html"},{"key":"2327_CR13","doi-asserted-by":"publisher","unstructured":"Burningham Z, Hashibe M, Spector L, et\u00a0al (2012) The epidemiology of sarcoma. Clinical Sarcoma Research 2(1https:\/\/doi.org\/10.1186\/2045-3329-2-14","DOI":"10.1186\/2045-3329-2-14"},{"key":"2327_CR14","doi-asserted-by":"publisher","unstructured":"de\u00a0Campos CP, Zeng Z, Ji Q (2009) Structure learning of bayesian networks using constraints. In: Proceedings of the 26th Annual International Conference on Machine Learning. ACM, New York, NY, USA, ICML \u201909, p 113\u2013120.https:\/\/doi.org\/10.1145\/1553374.1553389","DOI":"10.1145\/1553374.1553389"},{"key":"2327_CR15","doi-asserted-by":"publisher","unstructured":"de\u00a0Campos LM, Castellano JG (2007) Bayesian network learning algorithms using structural restrictions. Int J Approx Reason 45(2):233\u2013254. https:\/\/doi.org\/10.1016\/j.ijar.2006.06.009","DOI":"10.1016\/j.ijar.2006.06.009"},{"key":"2327_CR16","doi-asserted-by":"publisher","unstructured":"Cobb BR, Rum\u00ed R, Salmer\u00f3n A (2007) Bayesian Network Models with Discrete and Continuous Variables, Springer Berlin Heidelberg, Berlin, Heidelberg, pp 81\u201310https:\/\/doi.org\/10.1007\/978-3-540-68996-6_4","DOI":"10.1007\/978-3-540-68996-6_4"},{"key":"2327_CR17","unstructured":"Colombo D, Maathuis MH (2014) Order-independent constraint-based causal structure learning. J Mach Learn Res 15(1):3741\u20133782"},{"key":"2327_CR18","doi-asserted-by":"publisher","unstructured":"Constantinou AC, Guo Z, Kitson NK (2023) The impact of prior knowledge on causal structure learning. Knowl Inf Syst 65(8):3385\u20133434. https:\/\/doi.org\/10.1007\/s10115-023-01858-x","DOI":"10.1007\/s10115-023-01858-x"},{"key":"2327_CR19","doi-asserted-by":"publisher","unstructured":"Corander J, Hanage WP, Pensar J (2022) Causal discovery for the microbiome. The Lancet Microbe 3(11):e881\u2013e88https:\/\/doi.org\/10.1016\/s2666-5247(22)00186-0","DOI":"10.1016\/s2666-5247(22)00186-0"},{"key":"2327_CR20","doi-asserted-by":"publisher","unstructured":"Creech O, Krementz ET, Ryan RF, et\u00a0al (1958) Chemotherapy of cancer: Regional perfusion utilizing an extracorporeal circuit. Ann Surg 148(4):616\u2013632.https:\/\/doi.org\/10.1097\/00000658-195810000-00009","DOI":"10.1097\/00000658-195810000-00009"},{"key":"2327_CR21","doi-asserted-by":"publisher","unstructured":"Edwards D (2000) Introduction to Graphical Modelling. Springer New York,https:\/\/doi.org\/10.1007\/978-1-4612-0493-0","DOI":"10.1007\/978-1-4612-0493-0"},{"key":"2327_CR22","unstructured":"Efron B, Tibshirani R (1998) An introduction to the bootstrap, [nachdr.] edn. No.\u00a057 in Monographs on statistics and applied probability, Chapman & Hall, Boca Raton, Fla. [u.a.], originally publ. by Chapman & Hall"},{"key":"2327_CR23","unstructured":"Engelmann N, Linzner D, Koeppl H (2020) Continuous time Bayesian networks with clocks. In: III HD, Singh A (eds) Proceedings of the 37th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 119. PMLR, pp 2912\u20132921, https:\/\/proceedings.mlr.press\/v119\/engelmann20a.html"},{"key":"2327_CR24","doi-asserted-by":"publisher","unstructured":"Fenton N, Neil M (2018) Risk Assessment and Decision Analysis with Bayesian Networks. Chapman and Hall\/CRhttps:\/\/doi.org\/10.1201\/b21982","DOI":"10.1201\/b21982"},{"key":"2327_CR25","unstructured":"Fletcher C, Bridge JA, Hogendoorn PCW, et\u00a0al (2013) WHO classification of tumours of soft tissue and bone, 4th edn. World Health Organization classification of tumours, Internat. Agency for Research on Cancer, Lyon"},{"key":"2327_CR26","unstructured":"Fox J (2008) An R-and S-Plus companion to applied regression, [nachdr.] edn. Sage, Thousand Oaks, Calif. [u.a.]"},{"key":"2327_CR27","unstructured":"Friedman N, Goldszmidt M, Wyner A (1999) Data analysis with bayesian networks: a bootstrap approach. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. arXiv, San Francisco, CA, USA, UAI\u201999, pp 196\u2013205"},{"key":"2327_CR28","unstructured":"Gibbs P, Hiroshi S (1996) What is occam\u2019s razor. Usenet Physics FAQ"},{"key":"2327_CR29","doi-asserted-by":"publisher","unstructured":"Gilbert NF, Cannon CP, Lin PP, et\u00a0al (2009) Soft-tissue sarcoma. J Am Acad Orthop Surg 17(1):40\u20134https:\/\/doi.org\/10.5435\/00124635-200901000-00006","DOI":"10.5435\/00124635-200901000-00006"},{"key":"2327_CR30","doi-asserted-by":"crossref","unstructured":"Glocker B, Musolesi M, Richens J, et\u00a0al (2021) Causality in Digital Medicine. Nature Communications 12(1):5471","DOI":"10.1038\/s41467-021-25743-9"},{"key":"2327_CR31","doi-asserted-by":"publisher","unstructured":"Glymour C, Zhang K, Spirtes P (2019) Review of causal discovery methods based on graphical models. Front Genet 1https:\/\/doi.org\/10.3389\/fgene.2019.00524","DOI":"10.3389\/fgene.2019.00524"},{"key":"2327_CR32","unstructured":"Gonzales C, Journe A, Mabrouk A (2022) A hybrid algorithm for learning causal networks using uncertain experts\u2019 knowledge. In: Salmer\u00f3n A, Rum\u0131\u2019 R (eds) Proceedings of The 11$$^{\\text{th}}$$ International Conference on Probabilistic Graphical Models, Proceedings of Machine Learning Research, vol 186. PMLR, pp 241\u2013252, https:\/\/proceedings.mlr.press\/v186\/gonzales22a.html"},{"key":"2327_CR33","doi-asserted-by":"publisher","unstructured":"Gronchi A, Miceli R, Fiore M, et\u00a0al (2007) Extremity soft tissue sarcoma: Adding to the prognostic meaning of local failure. Ann Surg Oncol 14(5):1583\u2013159https:\/\/doi.org\/10.1245\/s10434-006-9325-0","DOI":"10.1245\/s10434-006-9325-0"},{"key":"2327_CR34","doi-asserted-by":"publisher","unstructured":"Gr\u00fcnbaum D (2023) Causal modelling and validation based on observational data and domain knowledge. PhD thesihttps:\/\/doi.org\/10.5283\/EPUB.55067","DOI":"10.5283\/EPUB.55067"},{"key":"2327_CR35","doi-asserted-by":"publisher","unstructured":"Gustafson P, R\u00f6\u00f6ser B, Rydholm A (1991) Is local recurrence of minor importance for metastases in soft tissue sarcoma? Cancer 67(8):2083\u20132086.https:\/\/doi.org\/10.1002\/1097-0142(19910415)67:8%3C;2083::aid-cncr2820670813%3E;3.0.co;2-5","DOI":"10.1002\/1097-0142(19910415)67:8%3C;2083::aid-cncr2820670813%3E;3.0.co;2-5"},{"key":"2327_CR36","doi-asserted-by":"publisher","unstructured":"Heckerman D, Geiger D, Chickering DM (1995) Learning bayesian networks: The combination of knowledge and statistical data. Mach Learn 20(3):197\u2013243.https:\/\/doi.org\/10.1007\/bf00994016","DOI":"10.1007\/bf00994016"},{"key":"2327_CR37","doi-asserted-by":"publisher","unstructured":"Hern\u00e1n MA, Hern\u00e1ndez-D\u00edaz S, Robins JM (2004) A structural approach to selection bias. Epidemiology 15(5):615\u2013625.https:\/\/doi.org\/10.1097\/01.ede.0000135174.63482.43","DOI":"10.1097\/01.ede.0000135174.63482.43"},{"key":"2327_CR38","doi-asserted-by":"publisher","unstructured":"Hern\u00e1n MA, Sterne JAC, Higgins JPT, et\u00a0al (2024) A structural description of biases that generate immortal time. Epidemiology 36(1):107\u2013114.https:\/\/doi.org\/10.1097\/ede.0000000000001808","DOI":"10.1097\/ede.0000000000001808"},{"key":"2327_CR39","unstructured":"Huang B, Zhang K, Zhang J, et\u00a0al (2020) Causal discovery from heterogeneous\/nonstationary data. J Mach Learn Res 21(89):1\u201353. http:\/\/jmlr.org\/papers\/v21\/19-232.html"},{"key":"2327_CR40","doi-asserted-by":"publisher","unstructured":"Imoto S, Higuchi T, Goto T, et\u00a0al (2004) Combining microarrays and biological knowledge for estimating gene networks via bayesian networks. J Bioinform Comput Biol 02(01):77\u201398. https:\/\/doi.org\/10.1142\/s021972000400048x","DOI":"10.1142\/s021972000400048x"},{"key":"2327_CR41","unstructured":"Jaber A, Kocaoglu M, Shanmugam K, et\u00a0al (2020) Causal discovery from soft interventions with unknown targets: Characterization and learning. In: Larochelle H, Ranzato M, Hadsell R, et\u00a0al (eds) Advances in Neural Information Processing Systems, vol\u00a033. Curran Associates, Inc., pp 9551\u20139561, https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/6cd9313ed34ef58bad3fdd504355e72c-Paper.pdf"},{"key":"2327_CR42","unstructured":"Kahneman D, Sibony O, Sunstein C (2021) Noise: A Flaw in Human Judgment. Little, Brown, https:\/\/books.google.it\/books?id=fhIBEAAAQBAJ"},{"key":"2327_CR43","doi-asserted-by":"publisher","unstructured":"Kelly J, Berzuini C, Keavney B, et\u00a0al (2022) A review of causal discovery methods for molecular network analysis. Mol Genet Genomic Med 10(10). https:\/\/doi.org\/10.1002\/mgg3.2055","DOI":"10.1002\/mgg3.2055"},{"key":"2327_CR44","doi-asserted-by":"publisher","unstructured":"Kitson NK, Constantinou AC (2023) Causal discovery using dynamically requested knowledghttps:\/\/doi.org\/10.2139\/ssrn.4620804","DOI":"10.2139\/ssrn.4620804"},{"key":"2327_CR45","unstructured":"Kocaoglu M, Shanmugam K, Bareinboim E (2017) Experimental design for learning causal graphs with latent variables. In: Guyon I, Luxburg UV, Bengio S, et\u00a0al (eds) Advances in Neural Information Processing Systems, vol\u00a030. Curran Associates, Inc., https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/291d43c696d8c3704cdbe0a72ade5f6c-Paper.pdf"},{"key":"2327_CR46","unstructured":"Koller D, Friedman N (2010) Probabilistic graphical models, [nachdr.] edn. Adaptive computation and machine learning, MIT Press, Cambridge, Mass. [u.a.], includes bibliographical references and index"},{"key":"2327_CR47","doi-asserted-by":"publisher","unstructured":"Lagakos SW (1979) General right censoring and its impact on the analysis of survival data. Biometrics 35(1):139. https:\/\/doi.org\/10.2307\/2529941","DOI":"10.2307\/2529941"},{"key":"2327_CR48","doi-asserted-by":"publisher","unstructured":"Lauritzen SL (1996) Graphical Models. Oxford University PressOxford,https:\/\/doi.org\/10.1093\/oso\/9780198522195.001.0001","DOI":"10.1093\/oso\/9780198522195.001.0001"},{"key":"2327_CR49","doi-asserted-by":"publisher","unstructured":"Maretty-Nielsen K, Aggerholm-Pedersen N, Safwat A, et\u00a0al (2014) Prognostic factors for local recurrence and mortality in adult soft tissue sarcoma of the extremities and trunk wall: A cohort study of 922 consecutive patients. Acta Orthop 85(3):323\u201333https:\/\/doi.org\/10.3109\/17453674.2014.908341","DOI":"10.3109\/17453674.2014.908341"},{"key":"2327_CR50","doi-asserted-by":"publisher","unstructured":"Mascaro S, Wu Y, Woodberry O, et\u00a0al (2023) Modeling covid-19 disease processes by remote elicitation of causal bayesian networks from medical experts. BMC Med Res Methodol 23(1https:\/\/doi.org\/10.1186\/s12874-023-01856-1","DOI":"10.1186\/s12874-023-01856-1"},{"key":"2327_CR51","doi-asserted-by":"publisher","unstructured":"Masegosa AR, Moral S (2013) An interactive approach for bayesian network learning using domain\/expert knowledge. Int J Approx Reason 54(8):1168\u20131181.https:\/\/doi.org\/10.1016\/j.ijar.2013.03.009","DOI":"10.1016\/j.ijar.2013.03.009"},{"key":"2327_CR52","doi-asserted-by":"publisher","unstructured":"Mathur S, Sutton J (2017) Personalized medicine could transform healthcare. Biomedical Reports 7(1):3\u2013https:\/\/doi.org\/10.3892\/br.2017.922","DOI":"10.3892\/br.2017.922"},{"key":"2327_CR53","doi-asserted-by":"publisher","unstructured":"Mbogu HM, Nicholson CD (2024) Data-driven root cause analysis via causal discovery using time-to-event data. Comput Ind Eng 190:109974.https:\/\/doi.org\/10.1016\/j.cie.2024.109974","DOI":"10.1016\/j.cie.2024.109974"},{"key":"2327_CR54","unstructured":"Meek C (1995) Causal inference and causal explanation with background knowledge. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, UAI\u201995, pp 403\u2013410"},{"key":"2327_CR55","unstructured":"Miller RG (2011) Survival analysis, wiley classics library ed (online-ausg.) edn. Wiley classics library, Wiley-Interscience, New York, includes bibliographical references and index. - Electronic reproduction; Palo Alto, Calif; ebrary; 2011; Available via World Wide Web; Access may be limited to ebrary affiliated libraries"},{"key":"2327_CR56","unstructured":"Murphy KP (2002) Dynamic bayesian networks: representation, inference and learning. PhD thesis, Computer Science, https:\/\/ibug.doc.ic.ac.uk\/media\/uploads\/documents\/courses\/DBN-PhDthesis-LongTutorail-Murphy.pdf"},{"key":"2327_CR57","unstructured":"Nodelman U, Shelton CR, Koller D (2002) Continuous time bayesian networks. In: Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, UAI\u201902, p 378\u2013387"},{"key":"2327_CR58","doi-asserted-by":"publisher","unstructured":"Nogueira AR, Abreu\u00a0Ferreira C, Gama J (2022) Temporal Nodes Causal Discovery for in Intensive Care Unit Survival Analysis, Springer International Publishing, pp 587\u201359https:\/\/doi.org\/10.1007\/978-3-031-16474-3_48","DOI":"10.1007\/978-3-031-16474-3_48"},{"key":"2327_CR59","doi-asserted-by":"publisher","unstructured":"Pearl J (1995) From Bayesian Networks to Causal Networks, Springer US, pp 157\u20131https:\/\/doi.org\/10.1007\/978-1-4899-1424-8sps9","DOI":"10.1007\/978-1-4899-1424-8sps9"},{"key":"2327_CR60","unstructured":"Pearl J (2020) The book of why, first trade paperback edition edn. Basic Books, New York, literaturverzeichnis: Seite 377-404"},{"key":"2327_CR61","unstructured":"Pearl J (2021) Causal inference in statistics, reprinted with revisions edn. Wiley, Chichester, literaturverzeichnis: Seite 127-131"},{"key":"2327_CR62","unstructured":"Peters J (2017) Elements of causal inference. Adaptive computation and machine learning, The MIT Press, Cambridge, Massachusetts"},{"key":"2327_CR63","doi-asserted-by":"publisher","unstructured":"Potter BK, Hwang PF, Forsberg JA, et\u00a0al (2013) Impact of margin status and local recurrence on soft-tissue sarcoma outcomes. The Journal of Bone & Joint Surgery 95(20):e15https:\/\/doi.org\/10.2106\/jbjs.l.01149","DOI":"10.2106\/jbjs.l.01149"},{"key":"2327_CR64","unstructured":"Rasmussen C, Ghahramani Z (2000) Occam\u2019s razor. In: Leen T, Dietterich T, Tresp V (eds) Advances in Neural Information Processing Systems, vol\u00a013. MIT Press, https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2000\/file\/0950ca92a4dcf426067cfd2246bb5ff3-Paper.pdf"},{"key":"2327_CR65","doi-asserted-by":"publisher","unstructured":"Ribeiro-Dantas MdC, Li H, Cabeli V, et\u00a0al (2024) Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients. iScience 27(5):109736.https:\/\/doi.org\/10.1016\/j.isci.2024.109736","DOI":"10.1016\/j.isci.2024.109736"},{"key":"2327_CR66","doi-asserted-by":"publisher","unstructured":"Rich JT, Neely JG, Paniello RC, et\u00a0al (2010) A practical guide to understanding kaplan-meier curves. Otolaryngology\u2013Head and Neck Surgery 143(3):331\u2013336.https:\/\/doi.org\/10.1016\/j.otohns.2010.05.007","DOI":"10.1016\/j.otohns.2010.05.007"},{"key":"2327_CR67","unstructured":"Roy S, Wong RKW, Ni Y (2023) Directed cyclic graph for causal discovery from multivariate functional data. In: Oh A, Naumann T, Globerson A, et\u00a0al (eds) Advances in Neural Information Processing Systems, vol\u00a036. Curran Associates, Inc., pp 42762\u201342774, https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2023\/file\/854a9ab0f323b841955e70ca383b27d1-Paper-Conference.pdf"},{"key":"2327_CR68","unstructured":"Russell SJ (2016) Artificial intelligence, third edition, global edition edn. Always learning, Pearson, Boston, \"Authorized adaption from the United States edition, entitled \u2019Artificial intelligence: a modern approach\u2019, third edition, ISBN 9780136042594, by Stuart J. Russell and Peter Norvig published by Pearson Education 2010.\" - R\u00fcckseite der Titelseite"},{"key":"2327_CR69","doi-asserted-by":"publisher","unstructured":"Saesen R, Van\u00a0Hemelrijck M, Bogaerts J, et\u00a0al (2023) Defining the role of real-world data in cancer clinical research: The position of the european organisation for research and treatment of cancer. Eur J Cancer 186:52\u201361.https:\/\/doi.org\/10.1016\/j.ejca.2023.03.013","DOI":"10.1016\/j.ejca.2023.03.013"},{"key":"2327_CR70","doi-asserted-by":"publisher","unstructured":"Sambri A, Caldari E, Fiore M, et\u00a0al (2021) Margin assessment in soft tissue sarcomas: Review of the literature. Cancers 13(7):1687.https:\/\/doi.org\/10.3390\/cancers13071687","DOI":"10.3390\/cancers13071687"},{"key":"2327_CR71","doi-asserted-by":"publisher","unstructured":"Scutari M (2020) Bayesian network models for incomplete and dynamic data. Stat Neerl 74(3):397\u201341https:\/\/doi.org\/10.1111\/stan.12197","DOI":"10.1111\/stan.12197"},{"key":"2327_CR72","doi-asserted-by":"publisher","unstructured":"Scutari M, Nagarajan R (2013) Identifying significant edges in graphical models of molecular networks. Artif Intell Med 57(3):207\u2013217.https:\/\/doi.org\/10.1016\/j.artmed.2012.12.006","DOI":"10.1016\/j.artmed.2012.12.006"},{"key":"2327_CR73","doi-asserted-by":"publisher","unstructured":"Scutari M, Graafland CE, Guti\u00e9rrez JM (2019) Who learns better bayesian network structures: Accuracy and speed of structure learning algorithms. Int J Approx Reason 115:235\u201325https:\/\/doi.org\/10.1016\/j.ijar.2019.10.003","DOI":"10.1016\/j.ijar.2019.10.003"},{"key":"2327_CR74","doi-asserted-by":"publisher","unstructured":"Sheidaei A, Foroushani AR, Gohari K, et\u00a0al (2022) A novel dynamic bayesian network approach for data mining and survival data analysis. BMC Med Inform Decis Mak 22(1https:\/\/doi.org\/10.1186\/s12911-022-02000-7","DOI":"10.1186\/s12911-022-02000-7"},{"key":"2327_CR75","doi-asserted-by":"publisher","unstructured":"Smolle MA, Tunn PU, Goldenitsch E, et\u00a0al (2017) The prognostic impact of unplanned excisions in a cohort of 728 soft tissue sarcoma patients: A multicentre study. Ann Surg Oncol 24(6):1596\u20131605.https:\/\/doi.org\/10.1245\/s10434-017-5776-8","DOI":"10.1245\/s10434-017-5776-8"},{"key":"2327_CR76","doi-asserted-by":"publisher","unstructured":"Sousa HS, Prieto-Castrillo F, Matos JC, et\u00a0al (2018) Combination of expert decision and learned based bayesian networks for multi-scale mechanical analysis of timber elements. Expert Syst Appl 93:156\u2013168.https:\/\/doi.org\/10.1016\/j.eswa.2017.09.060","DOI":"10.1016\/j.eswa.2017.09.060"},{"key":"2327_CR77","doi-asserted-by":"publisher","unstructured":"Spirtes P, Glymour C, Scheines R (2001) Causation, Prediction, and Search. The MIT Preshttps:\/\/doi.org\/10.7551\/mitpress\/1754.001.0001","DOI":"10.7551\/mitpress\/1754.001.0001"},{"key":"2327_CR78","doi-asserted-by":"publisher","unstructured":"Suchmacher M, Geller M (2012) Practical biostatistics. Elsevier, Amsterdam [u.a.https:\/\/doi.org\/10.1016\/c2011-0-04190-x, includes bibliographical references and index","DOI":"10.1016\/c2011-0-04190-x"},{"key":"2327_CR79","doi-asserted-by":"publisher","unstructured":"Tenenbaum JB, Griffiths TL, Niyogi S (2007) Intuitive Theories as Grammars for Causal Inference, Oxford University PressNew York, pp 301\u2013322.https:\/\/doi.org\/10.1093\/acprof:oso\/9780195176803.003.0020","DOI":"10.1093\/acprof:oso\/9780195176803.003.0020"},{"key":"2327_CR80","doi-asserted-by":"publisher","unstructured":"Therneau TM, Grambsch PM (2000) Multiple Events per Subject, Springer New York, pp 169\u201322https:\/\/doi.org\/10.1007\/978-1-4757-3294-8_8","DOI":"10.1007\/978-1-4757-3294-8_8"},{"key":"2327_CR81","doi-asserted-by":"publisher","unstructured":"Trojani M, Contesso G, Coindre JM, et\u00a0al (1984) Soft-tissue sarcomas of adults; study of pathological prognostic variables and definition of a histopathological grading system. Int J Cancer 33(1):37\u201342https:\/\/doi.org\/10.1002\/ijc.2910330108","DOI":"10.1002\/ijc.2910330108"},{"key":"2327_CR82","doi-asserted-by":"publisher","unstructured":"Tsamardinos I, Borboudakis G (2010) Permutation Testing Improves Bayesian Network Learning, Springer Berlin Heidelberg, pp 322\u2013337.https:\/\/doi.org\/10.1007\/978-3-642-15939-8_21","DOI":"10.1007\/978-3-642-15939-8_21"},{"key":"2327_CR83","unstructured":"Tu R, Zhang C, Ackermann P, et\u00a0al (2019) Causal discovery in the presence of missing data. In: Chaudhuri K, Sugiyama M (eds) Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol\u00a089. PMLR, pp 1762\u20131770, https:\/\/proceedings.mlr.press\/v89\/tu19a.html"},{"key":"2327_CR84","doi-asserted-by":"publisher","unstructured":"VanderWeele TJ, Ding P (2017) Sensitivity analysis in observational research: Introducing the e-value. Annals of Internal Medicine 167(4):268\u2013274https:\/\/doi.org\/10.7326\/m16-2607","DOI":"10.7326\/m16-2607"},{"key":"2327_CR85","doi-asserted-by":"publisher","unstructured":"Verma T, Pearl J (1990) Causal Networks: Semantics and Expressiveness, Elsevier, pp 69\u20137https:\/\/doi.org\/10.1016\/b978-0-444-88650-7.50011-1","DOI":"10.1016\/b978-0-444-88650-7.50011-1"},{"key":"2327_CR86","doi-asserted-by":"publisher","unstructured":"Vonk MC, Malekovic N, B\u00e4ck T, et\u00a0al (2023) Disentangling causality: assumptions in causal discovery and inference. Artificial Intelligence Review 56(9):10613\u20131064https:\/\/doi.org\/10.1007\/s10462-023-10411-9","DOI":"10.1007\/s10462-023-10411-9"},{"key":"2327_CR87","doi-asserted-by":"publisher","unstructured":"Vowels MJ, Camgoz NC, Bowden R (2022) D\u2019ya like dags? a survey on structure learning and causal discovery. ACM Computing Surveys 55(4):1\u20133https:\/\/doi.org\/10.1145\/3527154","DOI":"10.1145\/3527154"},{"key":"2327_CR88","doi-asserted-by":"publisher","unstructured":"\u0160tajduhar I, Dalbelo-Ba\u0161i\u0107 B (2010) Learning bayesian networks from survival data using weighting censored instances. J Biomed Inform 43(4):613\u201362https:\/\/doi.org\/10.1016\/j.jbi.2010.03.005","DOI":"10.1016\/j.jbi.2010.03.005"},{"key":"2327_CR89","doi-asserted-by":"publisher","unstructured":"\u0160tajduhar I, Dalbelo-Ba\u0161i\u0107 B, Bogunovi\u0107 N (2009) Impact of censoring on learning bayesian networks in survival modelling. Artif Intell Med 47(3):199\u201321https:\/\/doi.org\/10.1016\/j.artmed.2009.08.001","DOI":"10.1016\/j.artmed.2009.08.001"},{"key":"2327_CR90","doi-asserted-by":"publisher","unstructured":"Weskamp P, Ufton D, Drysch M, et\u00a0al (2022) Risk factors for occurrence and relapse of soft tissue sarcoma. Cancers 14(5):1273.https:\/\/doi.org\/10.3390\/cancers14051273","DOI":"10.3390\/cancers14051273"},{"key":"2327_CR91","doi-asserted-by":"publisher","unstructured":"Xu JG, Zhao Y, Chen J, et\u00a0al (2015) A structure learning algorithm for bayesian network using prior knowledge. J Comput Sci Technol 30(4):713\u2013724.https:\/\/doi.org\/10.1007\/s11390-015-1556-8","DOI":"10.1007\/s11390-015-1556-8"},{"key":"2327_CR92","unstructured":"Zanga A, Bernasconi A, Lucas PJF, et\u00a0al (2022a) Risk assessment of lymph node metastases in endometrial cancer patients: A causal approach. In: HC@AIxIA 2022. CEUR, CEUR workshop proceedings, pp 1\u201315, https:\/\/ceur-ws.org\/Vol-3307\/paper1.pdf"},{"key":"2327_CR93","doi-asserted-by":"publisher","unstructured":"Zanga A, Ozkirimli E, Stella F (2022b) A survey on causal discovery: Theory and practice. Int J Approx Reason 151:101\u2013129.https:\/\/doi.org\/10.1016\/j.ijar.2022.09.004","DOI":"10.1016\/j.ijar.2022.09.004"},{"key":"2327_CR94","doi-asserted-by":"publisher","unstructured":"Zanga A, Bernasconi A, Lucas PJF, et\u00a0al (2023) Causal Discovery with Missing Data in a Multicentric Clinical Study, Springer Nature Switzerland, pp 40\u20134https:\/\/doi.org\/10.1007\/978-3-031-34344-5_5","DOI":"10.1007\/978-3-031-34344-5_5"},{"key":"2327_CR95","doi-asserted-by":"publisher","unstructured":"Zhang X, Stamey JD, Mathur MB (2020) Assessing the impact of unmeasured confounders for credible and reliable real-world evidence. Pharmacoepidemiology and Drug Safety 29(10):1219\u20131227https:\/\/doi.org\/10.1002\/pds.5117","DOI":"10.1002\/pds.5117"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02327-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-025-02327-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-025-02327-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:51:18Z","timestamp":1768531878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-025-02327-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":95,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2327"],"URL":"https:\/\/doi.org\/10.1007\/s10916-025-02327-4","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]},"assertion":[{"value":"30 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"9"}}