{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T22:46:17Z","timestamp":1752101177141,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":18,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981403"},{"type":"electronic","value":"9789819981410"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8141-0_42","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T09:02:16Z","timestamp":1700902936000},"page":"564-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Handling Class Imbalance in\u00a0Forecasting Parkinson\u2019s Disease Wearing-off with\u00a0Fitness Tracker Dataset"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6800-3900","authenticated-orcid":false,"given":"John Noel","family":"Victorino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1109-8130","authenticated-orcid":false,"given":"Sozo","family":"Inoue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8766-4250","authenticated-orcid":false,"given":"Tomohiro","family":"Shibata","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"issue":"12","key":"42_CR1","doi-asserted-by":"publisher","first-page":"2169","DOI":"10.1002\/mds.23875","volume":"26","author":"A Antonini","year":"2011","unstructured":"Antonini, A., et al.: Wearing-off scales in Parkinson\u2019s disease: critique and recommendations: scales to assess wearing-off in PD. Mov. Disord. 26(12), 2169\u20132175 (2011). https:\/\/doi.org\/10.1002\/mds.23875","journal-title":"Mov. Disord."},{"doi-asserted-by":"publisher","unstructured":"Bhidayasiri, R., Tarsy, D.: Parkinson\u2019s disease: Hoehn and Yahr Scale. In: Bhidayasiri, R., Tarsy, D. (eds.) Movement Disorders: A Video Atlas: A Video Atlas, pp. 4\u20135. Current Clinical Neurology, Humana Press (2012). https:\/\/doi.org\/10.1007\/978-1-60327-426-5_2","key":"42_CR2","DOI":"10.1007\/978-1-60327-426-5_2"},{"doi-asserted-by":"publisher","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, pp. 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785, https:\/\/arxiv.org\/abs\/1603.02754","key":"42_CR3","DOI":"10.1145\/2939672.2939785"},{"doi-asserted-by":"publisher","unstructured":"Colombo, D., et al.: The \"gender factor\" in wearing-off among patients with Parkinson\u2019s disease: a post hoc analysis of DEEP study (2015). https:\/\/doi.org\/10.1155\/2015\/787451","key":"42_CR4","DOI":"10.1155\/2015\/787451"},{"unstructured":"Garmin: Garmin vivosmart 4. https:\/\/buy.garmin.com\/en-US\/US\/p\/605739","key":"42_CR5"},{"unstructured":"Garmin: V\u00edvosmart 4 - heart rate variability and stress level. https:\/\/www8.garmin.com\/manuals\/webhelp\/vivosmart4\/EN-US\/GUID-9282196F-D969-404D-B678-F48A13D8D0CB.html","key":"42_CR6"},{"unstructured":"Heyn, S., Davis, C.P.: Parkinson\u2019s disease early and later symptoms, 5 stages, and prognosis. https:\/\/www.medicinenet.com\/parkinsons_disease\/article.htm","key":"42_CR7"},{"issue":"6","key":"42_CR8","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1080\/08870449708406741","volume":"12","author":"C Jenkinson","year":"1997","unstructured":"Jenkinson, C., Fitzpatrick, R., Peto, V., Greenhall, R., Hyman, N.: The PDQ-8: development and validation of a short-form Parkinson\u2019s disease questionnaire. Psychol. Health 12(6), 805\u2013814 (1997). https:\/\/doi.org\/10.1080\/08870449708406741","journal-title":"Psychol. Health"},{"unstructured":"Kashiwara, K., Takeda, A., Maeda, T.: Learning Parkinson\u2019s disease together with patients: toward a medical practice that works with patients, with Q &A. Nankodo, Tokyo, Japan (2013). https:\/\/honto.jp\/netstore\/pd-book_25644244.html","key":"42_CR9"},{"unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a Python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1\u20135 (2017). https:\/\/jmlr.org\/papers\/v18\/16-365.html","key":"42_CR10"},{"key":"42_CR11","doi-asserted-by":"publisher","DOI":"10.1201\/9781315119588","volume-title":"Statistical Regression and Classification: From Linear Models to Machine Learning","author":"N Matloff","year":"2017","unstructured":"Matloff, N.: Statistical Regression and Classification: From Linear Models to Machine Learning. Chapman and Hall\/CRC, New York (2017). https:\/\/doi.org\/10.1201\/9781315119588"},{"issue":"12","key":"42_CR12","doi-asserted-by":"publisher","first-page":"e0243214","DOI":"10.1371\/journal.pone.0243214","volume":"15","author":"NJ Mouritzen","year":"2020","unstructured":"Mouritzen, N.J., Larsen, L.H., Lauritzen, M.H., Kj\u00e6r, T.W.: Assessing the performance of a commercial multisensory sleep tracker. PLoS ONE 15(12), e0243214 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0243214","journal-title":"PLoS ONE"},{"issue":"6","key":"42_CR13","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1097\/01.WNF.0000232277.68501.08","volume":"29","author":"M Stacy","year":"2006","unstructured":"Stacy, M., et al.: End-of-dose wearing off in Parkinson disease: a 9-question survey assessment. Clin. Neuropharmacol. 29(6), 312\u2013321 (2006). https:\/\/doi.org\/10.1097\/01.WNF.0000232277.68501.08","journal-title":"Clin. Neuropharmacol."},{"doi-asserted-by":"crossref","unstructured":"Stevens, S., Siengsukon, C.: Commercially-available wearable provides valid estimate of sleep stages (P3.6-042). Neurology 92 (2019). https:\/\/n.neurology.org\/content\/92\/15_Supplement\/P3.6-042","key":"42_CR14","DOI":"10.1212\/WNL.92.15_supplement.P3.6-042"},{"issue":"2","key":"42_CR15","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.parkreldis.2013.10.027","volume":"20","author":"F Stocchi","year":"2014","unstructured":"Stocchi, F., et al.: Early DEtection of wEaring off in Parkinson disease: The DEEP study. Parkinsonism Relat. Disord. 20(2), 204\u2013211 (2014). https:\/\/doi.org\/10.1016\/j.parkreldis.2013.10.027","journal-title":"Parkinsonism Relat. Disord."},{"issue":"16","key":"42_CR16","doi-asserted-by":"publisher","first-page":"7354","DOI":"10.3390\/app11167354","volume":"11","author":"JN Victorino","year":"2021","unstructured":"Victorino, J.N., Shibata, Y., Inoue, S., Shibata, T.: Predicting wearing-off of Parkinson\u2019s disease patients using a wrist-worn fitness tracker and a smartphone: a case study. Appl. Sci. 11(16), 7354 (2021). https:\/\/doi.org\/10.3390\/app11167354","journal-title":"Appl. Sci."},{"key":"42_CR17","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.procs.2021.12.064","volume":"196","author":"JN Victorino","year":"2022","unstructured":"Victorino, J.N., Shibata, Y., Inoue, S., Shibata, T.: Understanding wearing-off symptoms in Parkinson\u2019s disease patients using wrist-worn fitness tracker and a smartphone. Procedia Comput. Sci. 196, 684\u2013691 (2022). https:\/\/doi.org\/10.1016\/j.procs.2021.12.064","journal-title":"Procedia Comput. Sci."},{"doi-asserted-by":"crossref","unstructured":"Victorino, J.N., Shibata, Y., Inoue, S., Shibata, T.: Forecasting Parkinson\u2019s disease patients\u2019 wearing-off using wrist-worn fitness tracker and smartphone dataset (2023)","key":"42_CR18","DOI":"10.1201\/9781003371540-2"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8141-0_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T15:44:34Z","timestamp":1710344674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8141-0_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981403","9789819981410"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8141-0_42","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}