{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:34:48Z","timestamp":1742945688916,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031492655"},{"type":"electronic","value":"9783031492662"}],"license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"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\/978-3-031-49266-2_14","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T12:03:14Z","timestamp":1701432194000},"page":"200-207","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing Machine Learning Algorithms for\u00a0Medical Time-Series Data"],"prefix":"10.1007","author":[{"given":"Alex","family":"Helmersson","sequence":"first","affiliation":[]},{"given":"Faton","family":"Hoti","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Levander","sequence":"additional","affiliation":[]},{"given":"Aliasgar","family":"Shereef","sequence":"additional","affiliation":[]},{"given":"Emil","family":"Svensson","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0838-7951","authenticated-orcid":false,"given":"Ali","family":"El-Merhi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6566-5064","authenticated-orcid":false,"given":"Richard","family":"Vithal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6321-1839","authenticated-orcid":false,"given":"Jaquette","family":"Liljencrantz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7736-0093","authenticated-orcid":false,"given":"Linda","family":"Block","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5146-0205","authenticated-orcid":false,"given":"Helena Odenstedt","family":"Herg\u00e8s","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9052-0864","authenticated-orcid":false,"given":"Miroslaw","family":"Staron","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"14_CR1","series-title":"Springer Topics in Signal Processing","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-00296-0_5","volume-title":"Noise Reduction in Speech Processing","author":"J Benesty","year":"2009","unstructured":"Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Cohen, I., Huang, Y., Chen, J., Benesty, J. (eds.) Noise Reduction in Speech Processing. Springer Topics in Signal Processing, vol. 2, pp. 1\u20134. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-00296-0_5"},{"issue":"7","key":"14_CR2","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1111\/aas.13582","volume":"64","author":"SB Wenneberg","year":"2020","unstructured":"Wenneberg, S.B., et al.: Heart rate variability monitoring for the detection of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage. Acta Anaesthesiol. Scand. 64(7), 945\u2013952 (2020)","journal-title":"Acta Anaesthesiol. Scand."},{"issue":"9","key":"14_CR3","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1111\/aas.13657","volume":"64","author":"L Block","year":"2020","unstructured":"Block, L., El-Merhi, A., Liljencrantz, J., Naredi, S., Staron, M., Herg\u00e8s, H.O.: Cerebral ischemia detection using artificial intelligence (CIDAI)-a study protocol. Acta Anaesthesiol. Scand. 64(9), 1335\u20131342 (2020)","journal-title":"Acta Anaesthesiol. Scand."},{"issue":"1","key":"14_CR4","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","volume":"24","author":"Tak chung Fu","year":"2011","unstructured":"Tak chung Fu: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164\u2013181 (2011)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"14_CR5","unstructured":"Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93(5), 1043\u20131065 (1996)"},{"key":"14_CR6","unstructured":"Fagius, J., ten-Magnus Aquilonius, S.: Chapter 9 Cerebrovaskul\u00e4ra sjukdomar. Stockholm Liber (2006)"},{"issue":"1","key":"14_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1177\/17474930211065917","volume":"17","author":"VL Feigin","year":"2022","unstructured":"Feigin, V.L., et al.: World stroke organization (WSO): global stroke fact sheet 2022. Int. J. Stroke 17(1), 18\u201329 (2022)","journal-title":"Int. J. Stroke"},{"issue":"2","key":"14_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/cc11230","volume":"16","author":"B Foreman","year":"2012","unstructured":"Foreman, B., Claassen, J.: Quantitative EEG for the detection of brain ischemia. Crit. Care 16(2), 1\u20139 (2012)","journal-title":"Crit. Care"},{"key":"14_CR9","unstructured":"Martini, F., Nath, J.L.: The Brain and Cranial Nerves. Stockholm Liber (2006)"},{"issue":"4","key":"14_CR10","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1161\/STROKEAHA.120.032546","volume":"52","author":"M Megjhani","year":"2021","unstructured":"Megjhani, M., et al.: Dynamic detection of delayed cerebral ischemia. Stroke 52(4), 1370\u20131379 (2021)","journal-title":"Stroke"},{"key":"14_CR11","first-page":"185","volume":"61","author":"A Moerman","year":"2010","unstructured":"Moerman, A., Wouters, P.: Near-infrared spectroscopy (NIRS) monitoring in contemporary anesthesia and critical care. Acta Anaesthesiol. Belgica 61, 185\u201394 (2010)","journal-title":"Acta Anaesthesiol. Belgica"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.infsof.2019.06.003","volume":"115","author":"M Ochodek","year":"2019","unstructured":"Ochodek, M., Staron, M., Meding, W.: SimSAX: a measure of project similarity based on symbolic approximation method and software defect inflow. Inf. Softw. Technol. 115, 131\u2013147 (2019)","journal-title":"Inf. Softw. Technol."},{"issue":"2","key":"14_CR13","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1111\/ane.13541","volume":"145","author":"HO Herg\u00e8s","year":"2022","unstructured":"Herg\u00e8s, H.O., Vithal, R., El-Merhi, A., Naredi, S., Staron, M., Block, L.: Machine learning analysis of heart rate variability to detect delayed cerebral ischemia in subarachnoid hemorrhage. Acta Neurol. Scand. 145(2), 151\u2013159 (2022)","journal-title":"Acta Neurol. Scand."},{"issue":"19","key":"14_CR14","doi-asserted-by":"publisher","first-page":"6980","DOI":"10.3390\/app10196980","volume":"10","author":"K Song","year":"2020","unstructured":"Song, K., Ryu, M., Lee, K.: Transitional sax representation for knowledge discovery for time series. Appl. Sci. 10(19), 6980 (2020)","journal-title":"Appl. Sci."},{"key":"14_CR15","unstructured":"Tavenard, R.: An introduction to dynamic time warping (2021). https:\/\/rtavenar.github.io\/blog\/dtw.html"},{"issue":"1","key":"14_CR16","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.artmed.2008.11.007","volume":"45","author":"P Tormene","year":"2009","unstructured":"Tormene, P., Giorgino, T., Quaglini, S., Stefanelli, M.: Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif. Intell. Med. 45(1), 11\u201334 (2009)","journal-title":"Artif. Intell. Med."}],"container-title":["Lecture Notes in Computer Science","Product-Focused Software Process Improvement"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49266-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T12:05:55Z","timestamp":1701432355000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49266-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,2]]},"ISBN":["9783031492655","9783031492662"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49266-2_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,12,2]]},"assertion":[{"value":"2 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PROFES","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Product-Focused Software Process Improvement","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dornbirn","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","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":"11 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"profes2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.researchr.org\/home\/profes-2023","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":"82","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":"27","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":"13","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":"33% - 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":"2.8","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)"}}]}}