{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T22:38:29Z","timestamp":1772491109276,"version":"3.50.1"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030726928","type":"print"},{"value":"9783030726935","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":89,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Thanks to its ability to offer a time-oriented perspective on the clinical events that define the patient\u2019s path of care, Process Mining (PM) is assuming an emerging role in clinical data analytics. PM\u2019s ability to exploit time-series data and to build processes without any <jats:italic>a priori<\/jats:italic> knowledge suggests interesting synergies with the most common statistical analyses in healthcare, in particular survival analysis. In this work we demonstrate contributions of our process-oriented approach in analyzing a real-world retrospective dataset of patients treated for advanced melanoma at the Lausanne University Hospital. Addressing the clinical questions raised by our oncologists, we integrated PM in almost all the steps of a common statistical analysis. We show: (1) how PM can be leveraged to improve the quality of the data (data cleaning\/pre-processing), (2) how PM can provide efficient data visualizations that support and\/or suggest clinical hypotheses, also allowing to check the consistency between real and expected processes (descriptive statistics), and (3) how PM can assist in querying or re-expressing the data in terms of pre-defined reference workflows for testing survival differences among sub-cohorts (statistical inference). We exploit a rich set of PM tools for querying the event logs, inspecting the processes using statistical hypothesis testing, and performing conformance checking analyses to identify patterns in patient clinical paths and study the effects of different treatment sequences in our cohort.<\/jats:p>","DOI":"10.1007\/978-3-030-72693-5_22","type":"book-chapter","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T17:03:04Z","timestamp":1617123784000},"page":"291-304","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Process Mining Approach to Statistical Analysis: Application to a Real-World Advanced Melanoma Dataset"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6188-6413","authenticated-orcid":false,"given":"Erica","family":"Tavazzi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6007-1746","authenticated-orcid":false,"given":"Camille L.","family":"Gerard","sequence":"additional","affiliation":[]},{"given":"Olivier","family":"Michielin","sequence":"additional","affiliation":[]},{"given":"Alexandre","family":"Wicky","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4716-9925","authenticated-orcid":false,"given":"Roberto","family":"Gatta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0754-3425","authenticated-orcid":false,"given":"Michel A.","family":"Cuendet","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.procs.2019.12.189","volume":"164","author":"W van der Aalst","year":"2019","unstructured":"van der Aalst, W.: A practitioner\u2019s guide to process mining: limitations of the directly-follows graph. Procedia Comput. Sci. 164, 321\u2013328 (2019)","journal-title":"Procedia Comput. Sci."},{"key":"22_CR2","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-642-28108-2_19","volume-title":"Business Process Management Workshops","author":"W van der Aalst","year":"2012","unstructured":"van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 169\u2013194. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28108-2_19"},{"issue":"63","key":"22_CR3","first-page":"1122","volume":"2","author":"J Bulliard","year":"2006","unstructured":"Bulliard, J., Panizzon, R., Levi, F.: Melanoma prevention in Switzerland: where do we stand? Revue medicale suisse 2(63), 1122\u20131125 (2006)","journal-title":"Revue medicale suisse"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Cowey, C.L., Liu, F.X., Boyd, M., Aguilar, K.M., Krepler, C.: Real-world treatment patterns and clinical outcomes among patients with advanced melanoma: a retrospective, community oncology-based cohort study (A STROBE-compliant article). Medicine (Baltimore) 98(28), e16328 (2019)","DOI":"10.1097\/MD.0000000000016328"},{"key":"22_CR5","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-319-59758-4_42","volume-title":"Artificial Intelligence in Medicine","author":"R Gatta","year":"2017","unstructured":"Gatta, R., et al.: pMineR: an innovative R library for performing process mining in medicine. In: ten Teije, A., Popow, C., Holmes, J.H., Sacchi, L. (eds.) AIME 2017. LNCS (LNAI), vol. 10259, pp. 351\u2013355. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59758-4_42"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Gatta, R., Vallati, M., Lenkowicz, J., et al.: Generating and comparing knowledge graphs of medical processes using pMineR. In: Proceedings of the Knowledge Capture Conference. K-CAP 2017, Association for Computing Machinery, New York (2017)","DOI":"10.1145\/3148011.3154464"},{"key":"22_CR7","unstructured":"Geleijnse, G., Aklecha, H., et al.: Using process mining to evaluate colon cancer guideline adherence with cancer registry data: a case study. In: AMIA (2018)"},{"key":"22_CR8","unstructured":"Homayounfar, P.: Process mining challenges in hospital information systems. In: 2012 Federated Conference on Computer Science and Information Systems, pp. 1135\u20131140. IEEE (2012)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Kurniati, A.P., Johnson, O., Hogg, D., Hall, G.: Process mining in oncology: a literature review. In: 2016 6th International Conference on Information Communication and Management, pp. 291\u2013297. IEEE (2016)","DOI":"10.1109\/INFOCOMAN.2016.7784260"},{"issue":"10","key":"22_CR10","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.1108\/MD-09-2017-0906","volume":"56","author":"J Lenkowicz","year":"2018","unstructured":"Lenkowicz, J., Gatta, R., et al.: Assessing the conformity to clinical guidelines in oncology: an example for the multidisciplinary management of locally advanced colorectal cancer treatment. Manage. Decis. 56(10), 2172\u20132186 (2018)","journal-title":"Manage. Decis."},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Mans, R., Schonenberg, H., Song, M., Aalst, W.V., Bakker, P.: Application of process mining in healthcare - a case study in a Dutch hospital. In: BIOSTEC (2008)","DOI":"10.1007\/978-3-540-92219-3_32"},{"key":"22_CR12","series-title":"SpringerBriefs in Business Process Management","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-16071-9","volume-title":"Process Mining in Healthcare","author":"RS Mans","year":"2015","unstructured":"Mans, R.S., van der Aalst, W.M.P., Vanwersch, R.J.B.: Process Mining in Healthcare. SBPM. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-16071-9"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Rinner, C., Helm, E., Dunkl, R., et al.: Process mining and conformance checking of long running processes in the context of melanoma surveillance. Int. J. Environ. Res. Public Health 15(12), 2809 (2018)","DOI":"10.3390\/ijerph15122809"}],"container-title":["Lecture Notes in Business Information Processing","Process Mining Workshops"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72693-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T17:06:33Z","timestamp":1617123993000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-72693-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030726928","9783030726935"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72693-5_22","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"31 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Process Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Padua","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpmconference.org\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"59","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"49% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference changed to an online format due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}