{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:19:21Z","timestamp":1762341561124,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009244","name":"Stockholm University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009244","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper, we study the problem of classification of sequences of temporal intervals. Our main contribution is a novel framework, which we call , for extracting relevant features from interval sequences to construct classifiers. introduces the notion of utilizing random temporal abstraction features, we define as , as a means to capture information pertaining to class-discriminatory events which occur across the span of complete interval sequences. Our empirical evaluation is applied to a wide array of benchmark data sets and fourteen novel datasets for adverse drug event detection. We demonstrate how the introduction of simple sequential features, followed by progressively more complex features each improve classification performance. Importantly, this investigation demonstrates that  significantly improves AUC performance over the current state-of-the-art. The investigation also reveals that the selection of underlying classification algorithm is important to achieve superior predictive performance, and how the number of features influences the performance of our framework.<\/jats:p>","DOI":"10.1007\/s10618-020-00719-3","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T10:26:18Z","timestamp":1606127178000},"page":"372-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["SMILE: a feature-based temporal abstraction framework for event-interval sequence classification"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8509-5376","authenticated-orcid":false,"given":"Jonathan","family":"Rebane","sequence":"first","affiliation":[]},{"given":"Isak","family":"Karlsson","sequence":"additional","affiliation":[]},{"given":"Leon","family":"Bornemann","sequence":"additional","affiliation":[]},{"given":"Panagiotis","family":"Papapetrou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"issue":"11","key":"719_CR1","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1145\/182.358434","volume":"26","author":"JF Allen","year":"1983","unstructured":"Allen JF (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832\u2013843","journal-title":"Commun ACM"},{"key":"719_CR2","doi-asserted-by":"crossref","unstructured":"Ayres J, Flannick J, Gehrke J, Yiu T (2002) Sequential pattern mining using a bitmap representation. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 429\u2013435","DOI":"10.1145\/775047.775109"},{"issue":"4","key":"719_CR3","first-page":"63","volume":"4","author":"I Batal","year":"2013","unstructured":"Batal I, Valizadegan H, Cooper GF, Hauskrecht M (2013) A temporal pattern mining approach for classifying electronic health record data. ACM Trans Intell Syst Technol (TIST) 4(4):63","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"719_CR4","doi-asserted-by":"crossref","unstructured":"Bornemann L, Lecerf J, Papapetrou P (2016) Stife: a framework for feature-based classification of sequences of temporal intervals. In: International conference on discovery science. Springer, pp 85\u2013100","DOI":"10.1007\/978-3-319-46307-0_6"},{"key":"719_CR5","unstructured":"Dalianis H, Henriksson A, Kvist M, Velupillai S, Weegar R (2015) Health bank: a workbench for data science applications in healthcare. In: CAiSE-2015 industry track co-located with 27th conference on advanced information systems engineering (CAiSE-CEUR), vol 1381, pp 1\u201318"},{"issue":"Jan","key":"719_CR6","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1\u201330","journal-title":"J Mach Learn Res"},{"key":"719_CR7","doi-asserted-by":"crossref","unstructured":"Giannotti F, Nanni M, Pedreschi D (2006) Efficient mining of temporally annotated sequences. In: Proceedings of the 6th SIAM data mining conference, vol 124, pp 348\u2013359","DOI":"10.1137\/1.9781611972764.31"},{"key":"719_CR8","doi-asserted-by":"crossref","unstructured":"H\u00f6ppner F, Klawonn F (2001) Finding informative rules in interval sequences. In: Proceedings of the 4th international symposium on advances in intelligent data analysis, pp 123\u2013132","DOI":"10.1007\/3-540-44816-0_13"},{"issue":"2","key":"719_CR9","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1111\/j.1365-2125.2006.02698.x","volume":"63","author":"R Howard","year":"2007","unstructured":"Howard R, Avery A, Slavenburg S, Royal S, Pipe G, Lucassen P, Pirmohamed M (2007) Which drugs cause preventable admissions to hospital? A systematic review. Br J Clin Pharmacol 63(2):136\u2013147","journal-title":"Br J Clin Pharmacol"},{"key":"719_CR10","doi-asserted-by":"crossref","unstructured":"Karlsson I, Bostr\u00f6m H (2016) Predicting adverse drug events using heterogeneous event sequences. In: 2016 IEEE international conference on healthcare informatics (ICHI). IEEE, pp 356\u2013362","DOI":"10.1109\/ICHI.2016.64"},{"issue":"5","key":"719_CR11","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s10618-016-0473-y","volume":"30","author":"I Karlsson","year":"2016","unstructured":"Karlsson I, Papapetrou P, Bostr\u00f6m H (2016) Generalized random shapelet forests. Data Min Knowl Discov 30(5):1053\u20131085. https:\/\/doi.org\/10.1007\/s10618-016-0473-y","journal-title":"Data Min Knowl Discov"},{"key":"719_CR12","first-page":"211","volume":"1","author":"R Kosara","year":"2001","unstructured":"Kosara R, Miksch S (2001) Visualizing complex notions of time. Stud Health Technol Inform 1:211\u2013215","journal-title":"Stud Health Technol Inform"},{"key":"719_CR13","doi-asserted-by":"crossref","unstructured":"Kostakis O, Papapetrou P (2017) Abide: querying time-evolving sequences of temporal intervals. In: International symposium on intelligent data analysis. Springer, pp 173\u2013185","DOI":"10.1007\/978-3-319-68765-0_15"},{"key":"719_CR14","doi-asserted-by":"crossref","unstructured":"Kostakis O, Papapetrou P, Hollm\u00e9n J (2011) Artemis: assessing the similarity of event-interval sequences. In: Proceedings of the conference on machine learning and knowledge discovery in databases (ECML\/PKDD 2011), pp 229\u2013244","DOI":"10.1007\/978-3-642-23783-6_15"},{"key":"719_CR15","unstructured":"Kostakis OK, Gionis AG (2015) Subsequence search in event-interval sequences. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 851\u2013854"},{"key":"719_CR16","doi-asserted-by":"crossref","unstructured":"Kotsifakos A, Papapetrou P, Athitsos V (2013) Ibsm: interval-based sequence matching. In: Proceedings of the SIAM conference on data mining (SDM)","DOI":"10.1137\/1.9781611972832.66"},{"issue":"1","key":"719_CR17","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1002\/sam.11192","volume":"7","author":"HT Lam","year":"2014","unstructured":"Lam HT, M\u00f6rchen F, Fradkin D, Calders T (2014) Mining compressing sequential patterns. Stat Anal Data Min ASA Data Sci J 7(1):34\u201352","journal-title":"Stat Anal Data Min ASA Data Sci J"},{"issue":"9","key":"719_CR18","doi-asserted-by":"publisher","first-page":"1188","DOI":"10.1109\/TKDE.2007.1055","volume":"19","author":"S Laxman","year":"2007","unstructured":"Laxman S, Sastry P, Unnikrishnan K (2007) Discovering frequent generalized episodes when events persist for different durations. IEEE Trans Knowl Data Eng 19(9):1188\u20131201. https:\/\/doi.org\/10.1109\/TKDE.2007.1055","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"719_CR19","doi-asserted-by":"crossref","unstructured":"Lin JL (2003) Mining maximal frequent intervals. In: Proceedings of the 18th ACM symposium on applied computing, pp 624\u2013629","DOI":"10.1145\/952532.952617"},{"key":"719_CR20","doi-asserted-by":"crossref","unstructured":"Menger RP, Dossani RH, Thakur JD, Farokhi F, Morrow K, Guthikonda B (2015) Extra-axial hematoma and trimethoprim-sulfamethoxazole induced aplastic anemia: the role of hematological diseases in subdural and epidural hemorrhage. Case reports in hematology 2015","DOI":"10.1155\/2015\/374951"},{"key":"719_CR21","doi-asserted-by":"crossref","unstructured":"Mooney C, Roddick JF (2004) Mining relationships between interacting episodes. In: Proceedings of the 4th SIAM international conference on data mining","DOI":"10.1137\/1.9781611972740.1"},{"key":"719_CR22","doi-asserted-by":"crossref","unstructured":"M\u00f6rchen F, Fradkin D (2010) Robust mining of time intervals with semi-interval partial order patterns. In: Proceedings of the 2010 SIAM international conference on data mining. SIAM, pp 315\u2013326","DOI":"10.1137\/1.9781611972801.28"},{"issue":"4","key":"719_CR23","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1007\/s10618-014-0380-z","volume":"29","author":"R Moskovitch","year":"2015","unstructured":"Moskovitch R, Shahar Y (2015a) Classification-driven temporal discretization of multivariate time series. Data Min Knowl Discov 29(4):871\u2013913","journal-title":"Data Min Knowl Discov"},{"issue":"1","key":"719_CR24","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s10115-014-0784-5","volume":"45","author":"R Moskovitch","year":"2015","unstructured":"Moskovitch R, Shahar Y (2015b) Classification of multivariate time series via temporal abstraction and time intervals mining. Knowl Inf Syst 45(1):35\u201374","journal-title":"Knowl Inf Syst"},{"key":"719_CR25","doi-asserted-by":"crossref","unstructured":"Moskovitch R, Walsh C, Wang F, Hripcsak G, Tatonetti N (2015) Outcomes prediction via time intervals related patterns. In: 2015 IEEE international conference on data mining (ICDM). IEEE, pp 919\u2013924","DOI":"10.1109\/ICDM.2015.143"},{"issue":"10","key":"719_CR26","doi-asserted-by":"publisher","first-page":"795","DOI":"10.7326\/0003-4819-140-10-200405180-00009","volume":"140","author":"JR Nebeker","year":"2004","unstructured":"Nebeker JR, Barach P, Samore MH (2004) Clarifying adverse drug events: a clinician\u2019s guide to terminology, documentation, and reporting. Ann Intern Med 140(10):795\u2013801","journal-title":"Ann Intern Med"},{"issue":"3","key":"719_CR27","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1080\/09298219608570707","volume":"25","author":"F Pachet","year":"1996","unstructured":"Pachet F, Ramalho G, Carrive J (1996) Representing temporal musical objects and reasoning in the MusES system. J New Music Res 25(3):252\u2013275","journal-title":"J New Music Res"},{"key":"719_CR28","doi-asserted-by":"crossref","unstructured":"Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2005) Discovering frequent arrangements of temporal intervals. In: Proceedings of 5th IEEE international conference on data mining, pp 354\u2013361","DOI":"10.1109\/ICDM.2005.50"},{"key":"719_CR29","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10115-009-0196-0","volume":"21","author":"P Papapetrou","year":"2009","unstructured":"Papapetrou P, Kollios G, Sclaroff S, Gunopulos D (2009) Mining frequent arrangements of temporal intervals. Knowl Inf Syst 21:133\u2013171","journal-title":"Knowl Inf Syst"},{"key":"719_CR30","doi-asserted-by":"crossref","unstructured":"Patel D, Hsu W, Lee ML (2008) Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD international conference on management of data. ACM, pp 393\u2013404","DOI":"10.1145\/1376616.1376658"},{"key":"719_CR31","unstructured":"Rao KV (2014) eChapter 24. Drug-induced hematologic disorders. The McGraw-Hill Companies, New York. https:\/\/accesspharmacy.mhmedical.com\/content.aspx?aid=57525021"},{"issue":"1","key":"719_CR32","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1253\/circj.CJ-14-0582","volume":"79","author":"T Shikata","year":"2014","unstructured":"Shikata T, Sasaki N, Ueda M, Kimura T, Itohara K, Sugahara M, Fukui M, Manabe E, Masuyama T, Tsujino T (2014) Use of proton pump inhibitors is associated with anemia in cardiovascular outpatients. Circ J 79(1):193\u2013200. https:\/\/doi.org\/10.1253\/circj.CJ-14-0582","journal-title":"Circ J"},{"key":"719_CR33","unstructured":"Uddin MT, Uddiny MA (2015) Human activity recognition from wearable sensors using extremely randomized trees. In: 2015 International conference on electrical engineering and information communication technology (ICEEICT), pp 1\u20136"},{"issue":"1","key":"719_CR34","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.datak.2006.10.009","volume":"63","author":"E Winarko","year":"2007","unstructured":"Winarko E, Roddick JF (2007) Armada: an algorithm for discovering richer relative temporal association rules from interval-based data. Data Knowl Eng 63(1):76\u201390. https:\/\/doi.org\/10.1016\/j.datak.2006.10.009","journal-title":"Data Knowl Eng"},{"key":"719_CR35","unstructured":"Wistuba M, Grabocka J, Schmidt-Thieme L (2015) Ultra-fast shapelets for time series classification. arXiv preprint arXiv:1503.05018"},{"key":"719_CR36","doi-asserted-by":"publisher","unstructured":"Yan X, Han J, Afshar R (2003) Clospan: mining: closed sequential patterns in large datasets. https:\/\/doi.org\/10.1137\/1.9781611972733.15","DOI":"10.1137\/1.9781611972733.15"},{"key":"719_CR37","doi-asserted-by":"crossref","unstructured":"Ye L, Keogh E (2009) Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 947\u2013956","DOI":"10.1145\/1557019.1557122"},{"issue":"5","key":"719_CR38","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TKDE.2015.2510010","volume":"28","author":"C Zhou","year":"2015","unstructured":"Zhou C, Cule B, Goethals B (2015) Pattern based sequence classification. IEEE Trans Knowl Data Eng 28(5):1285\u20131298","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-020-00719-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-020-00719-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-020-00719-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T06:31:26Z","timestamp":1611124286000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-020-00719-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":38,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["719"],"URL":"https:\/\/doi.org\/10.1007\/s10618-020-00719-3","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2020,11,23]]},"assertion":[{"value":"2 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This work was partly supported by the VR-2016-03372 Swedish Research Council Starting Grant as well as by grants provided by Stockholm University and Stockholm County Council (SU-SLL). Ethical approval was granted by the Stockholm Regional Ethical Review Board under Permission No. 2012\/834-31\/5.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"The source code for the implementation of SMILE can be found at: .","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Source code"}}]}}