{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:11:51Z","timestamp":1742911911961,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030216412"},{"type":"electronic","value":"9783030216429"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-21642-9_49","type":"book-chapter","created":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T19:14:41Z","timestamp":1560885281000},"page":"386-396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Mining Compact Predictive Pattern Sets Using Classification Model"],"prefix":"10.1007","author":[{"given":"Matteo","family":"Mantovani","sequence":"first","affiliation":[]},{"given":"Carlo","family":"Combi","sequence":"additional","affiliation":[]},{"given":"Milos","family":"Hauskrecht","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,30]]},"reference":[{"key":"49_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of SIGMOD (1993)","DOI":"10.1145\/170035.170072"},{"key":"49_CR2","unstructured":"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of VLDB (1994)"},{"key":"49_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-642-33486-3_17","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"I Batal","year":"2012","unstructured":"Batal, I., Cooper, G., Hauskrecht, M.: A Bayesian scoring technique for mining predictive and non-spurious rules. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 260\u2013276. Springer, Heidelberg (2012). \nhttps:\/\/doi.org\/10.1007\/978-3-642-33486-3_17"},{"issue":"1","key":"49_CR4","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10115-015-0819-6","volume":"46","author":"I Batal","year":"2016","unstructured":"Batal, I., Cooper, G.F., Fradkin, D., Harrison, J., Moerchen, F., Hauskrecht, M.: An efficient pattern mining approach for event detection in multivariate temporal data. Knowl. Inf. Syst. 46(1), 115\u2013150 (2016)","journal-title":"Knowl. Inf. Syst."},{"key":"49_CR5","doi-asserted-by":"crossref","unstructured":"Batal, I., Fradkin, D., Harrison, J., Moerchen, F., Hauskrecht, M.: Mining recent temporal patterns for event detection in multivariate time series data. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining (SIGKDD) (2012)","DOI":"10.1145\/2339530.2339578"},{"key":"49_CR6","doi-asserted-by":"crossref","unstructured":"Batal, I., Hauskrecht, M.: Constructing classification features using minimal predictive patterns. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 869\u2013878. ACM (2010)","DOI":"10.1145\/1871437.1871549"},{"issue":"4","key":"49_CR7","first-page":"63:1","volume":"4","author":"I Batal","year":"2012","unstructured":"Batal, I., Valizadegan, H., Cooper, G.F., Hauskrecht, M.: A temporal pattern mining approach for classifying electronic health record data. ACM Trans. Intell. Syst. Technol. (ACM TIST) 4(4), 63:1\u201363:22 (2012). Spec. Issue Health Inform","journal-title":"ACM Trans. Intell. Syst. Technol. (ACM TIST)"},{"issue":"2","key":"49_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.ijmedinf.2006.11.006","volume":"77","author":"R Bellazzi","year":"2008","unstructured":"Bellazzi, R., Zupan, B.: Predictive data mining in clinical medicine: current issues and guidelines. Int. J. Med. Inform. 77(2), 81\u201397 (2008)","journal-title":"Int. J. Med. Inform."},{"issue":"6","key":"49_CR9","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1378\/chest.101.6.1644","volume":"101","author":"RC Bone","year":"1992","unstructured":"Bone, R.C., et al.: Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 101(6), 1644\u20131655 (1992)","journal-title":"Chest"},{"issue":"1","key":"49_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/cc2980","volume":"9","author":"S Brett","year":"2004","unstructured":"Brett, S.: Science review: the use of proton pump inhibitors for gastric acid suppression in critical illness. Crit. Care 9(1), 45 (2004)","journal-title":"Crit. Care"},{"issue":"2","key":"49_CR11","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s00134-012-2769-8","volume":"39","author":"RP Dellinger","year":"2013","unstructured":"Dellinger, R.P., et al.: Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 39(2), 165\u2013228 (2013)","journal-title":"Intensive Care Med."},{"issue":"8","key":"49_CR12","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"issue":"3","key":"49_CR13","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1145\/1132960.1132963","volume":"38","author":"L Geng","year":"2006","unstructured":"Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. (CSUR) 38(3), 9 (2006)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"49_CR14","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2011","unstructured":"Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York (2011)"},{"key":"49_CR15","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3 (2016)","DOI":"10.1038\/sdata.2016.35"},{"key":"49_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1007\/3-540-45329-6_8","volume-title":"Progress in Artificial Intelligence","author":"V Jovanoski","year":"2001","unstructured":"Jovanoski, V., Lavra\u010d, N.: Classification rule learning with APRIORI-C. In: Brazdil, P., Jorge, A. (eds.) EPIA 2001. LNCS (LNAI), vol. 2258, pp. 44\u201351. Springer, Heidelberg (2001). \nhttps:\/\/doi.org\/10.1007\/3-540-45329-6_8"},{"key":"49_CR17","unstructured":"Koller, D., Sahami, M.: Toward optimal feature selection. Technical report, Stanford InfoLab (1996)"},{"issue":"1","key":"49_CR18","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0933-3657(98)00062-1","volume":"16","author":"N Lavra\u010d","year":"1999","unstructured":"Lavra\u010d, N.: Selected techniques for data mining in medicine. Artif. Intell. Med. 16(1), 3\u201323 (1999)","journal-title":"Artif. Intell. Med."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-21642-9_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,13]],"date-time":"2020-02-13T01:46:59Z","timestamp":1581558419000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-21642-9_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030216412","9783030216429"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-21642-9_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"30 May 2019","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":"Conference on Artificial Intelligence in Medicine in Europe","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poznan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/aime19.aimedicine.info\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}