{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:15:30Z","timestamp":1743131730559,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031093418"},{"type":"electronic","value":"9783031093425"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-09342-5_30","type":"book-chapter","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:05:42Z","timestamp":1657285542000},"page":"310-320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["When Can I Expect the mHealth User to Return? Prediction Meets Time Series with Gaps"],"prefix":"10.1007","author":[{"given":"Miro","family":"Schleicher","sequence":"first","affiliation":[]},{"given":"R\u00fcdiger","family":"Pryss","sequence":"additional","affiliation":[]},{"given":"Winfried","family":"Schlee","sequence":"additional","affiliation":[]},{"given":"Myra","family":"Spiliopoulou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"issue":"3","key":"30_CR1","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/s10618-016-0483-9","volume":"31","author":"A Bagnall","year":"2016","unstructured":"Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3), 606\u2013660 (2016). https:\/\/doi.org\/10.1007\/s10618-016-0483-9","journal-title":"Data Min. Knowl. Discov."},{"key":"30_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.3389\/fnagi.2019.00053","volume":"11","author":"CR Cederroth","year":"2019","unstructured":"Cederroth, C.R., et al.: Towards an understanding of tinnitus heterogeneity. Front. Aging Neurosci. 11, 53 (2019)","journal-title":"Front. Aging Neurosci."},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, pp. 785\u2013794. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"30_CR4","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.artmed.2013.01.003","volume":"58","author":"F Cismondi","year":"2013","unstructured":"Cismondi, F., Fialho, A.S., Vieira, S.M., Reti, S.R., Sousa, J.M., Finkelstein, S.N.: Missing data in medical databases: impute, delete or classify? Artif. Intell. Med. 58(1), 63\u201372 (2013)","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"30_CR5","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7.1.e11","volume":"7","author":"G Eysenbach","year":"2005","unstructured":"Eysenbach, G.: The law of attrition. J. Med. Internet Res. 7(1), e402 (2005)","journal-title":"J. Med. Internet Res."},{"issue":"480","key":"30_CR6","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1198\/016214507000000860","volume":"102","author":"P Fryzlewicz","year":"2007","unstructured":"Fryzlewicz, P.: Unbalanced Haar technique for nonparametric function estimation. J. Am. Stat. Assoc. 102(480), 1318\u20131327 (2007)","journal-title":"J. Am. Stat. Assoc."},{"issue":"11","key":"30_CR7","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.6342","volume":"18","author":"CJ Hochheimer","year":"2016","unstructured":"Hochheimer, C.J., Sabo, R.T., Krist, A.H., Day, T., Cyrus, J., Woolf, S.H.: Methods for evaluating respondent attrition in web-based surveys. J. Med. Internet Res. 18(11), e301 (2016)","journal-title":"J. Med. Internet Res."},{"issue":"8","key":"30_CR8","doi-asserted-by":"publisher","DOI":"10.2196\/12811","volume":"21","author":"CJ Hochheimer","year":"2019","unstructured":"Hochheimer, C.J., Sabo, R.T., Perera, R.A., Mukhopadhyay, N., Krist, A.H.: Identifying attrition phases in survey data: applicability and assessment study. J. Med. Internet Res. 21(8), e12811 (2019)","journal-title":"J. Med. Internet Res."},{"key":"30_CR9","first-page":"186","volume":"7","author":"GF Jenks","year":"1967","unstructured":"Jenks, G.F.: The data model concept in statistical mapping. Int. Yearb. Cartogr. 7, 186\u2013190 (1967)","journal-title":"Int. Yearb. Cartogr."},{"key":"30_CR10","unstructured":"Keogh, E.J., Chu, S., Hart, D., Pazzani, M.J.: An online algorithm for segmenting time series. In: Proceedings of the 2001 IEEE International Conference on Data Mining, pp. 289\u2013296 (2001)"},{"issue":"500","key":"30_CR11","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1080\/01621459.2012.737745","volume":"107","author":"R Killick","year":"2012","unstructured":"Killick, R., Fearnhead, P., Eckley, I.A.: Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107(500), 1590\u20131598 (2012)","journal-title":"J. Am. Stat. Assoc."},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"294","DOI":"10.3389\/fnagi.2016.00294","volume":"8","author":"W Schlee","year":"2016","unstructured":"Schlee, W., et al.: Measuring the moment-to-moment variability of tinnitus: the TrackYourTinnitus smart phone app. Front. Aging Neurosci. 8, 294 (2016)","journal-title":"Front. Aging Neurosci."},{"issue":"1","key":"30_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-79527-0","volume":"10","author":"M Schleicher","year":"2020","unstructured":"Schleicher, M., et al.: Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring. Sci. Rep. 10(1), 1\u201313 (2020)","journal-title":"Sci. Rep."},{"key":"30_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eatbeh.2021.101509","volume":"41","author":"GA Williams-Kerver","year":"2021","unstructured":"Williams-Kerver, G.A., et al.: Baseline and momentary predictors of ecological momentary assessment adherence in a sample of adults with binge-eating disorder. Eat. Behav. 41, 101509 (2021)","journal-title":"Eat. Behav."},{"key":"30_CR15","unstructured":"World Health Organization and Others: Adherence to long-term therapies: evidence for action. World Health Organization (2003)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09342-5_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:09:45Z","timestamp":1657285785000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09342-5_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031093418","9783031093425"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09342-5_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"9 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Halifax, NS","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime22.aimedicine.info\/","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":"113","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":"39","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":"7","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":"35% - 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":"3","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)"}}]}}