{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:23:13Z","timestamp":1743124993569,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319993645"},{"type":"electronic","value":"9783319993652"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","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":[[2018]]},"DOI":"10.1007\/978-3-319-99365-2_34","type":"book-chapter","created":{"date-parts":[[2018,8,11]],"date-time":"2018-08-11T09:15:03Z","timestamp":1533978903000},"page":"388-399","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recognizing Diseases from Physiological Time Series Data Using Probabilistic Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4160-4989","authenticated-orcid":false,"given":"Danni","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4776-5292","authenticated-orcid":false,"given":"Li","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2087-4894","authenticated-orcid":false,"given":"Guoxin","family":"Su","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9972-8020","authenticated-orcid":false,"given":"Yande","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1883-2708","authenticated-orcid":false,"given":"Aamir","family":"Khan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,12]]},"reference":[{"key":"34_CR1","doi-asserted-by":"crossref","unstructured":"Allen, J.F.: Maintaining knowledge about temporal intervals. ACM (1983)","DOI":"10.1145\/182.358434"},{"issue":"5","key":"34_CR2","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1109\/JBHI.2015.2438645","volume":"19","author":"H Banaee","year":"2015","unstructured":"Banaee, H., Loutfi, A.: Data-driven rule mining and representation of temporal patterns in physiological sensor data. IEEE J. Biomed. Health Inform. 19(5), 1557\u20131566 (2015)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"34_CR3","unstructured":"Beumer, M.: Qualitative probabilistic networks in medical diagnosis (2006)"},{"issue":"1","key":"34_CR4","first-page":"1","volume":"01","author":"M Fatima","year":"2017","unstructured":"Fatima, M., Pasha, M.: Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 01(1), 1\u201316 (2017)","journal-title":"J. Intell. Learn. Syst. Appl."},{"issue":"1","key":"34_CR5","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","volume":"24","author":"TC Fu","year":"2011","unstructured":"Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164\u2013181 (2011)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"34_CR6","doi-asserted-by":"crossref","unstructured":"Goldin, D., Mardales, R., Nagy, G.: In search of meaning for time series subsequence clustering: matching algorithms based on a new distance measure, pp. 347\u2013356 (2006)","DOI":"10.1145\/1183614.1183666"},{"key":"34_CR7","first-page":"185","volume":"220","author":"J He","year":"2012","unstructured":"He, J., et al.: An association rule analysis framework for complex physiological and genetic data. J. Solid State Chem. 220, 185\u2013190 (2012)","journal-title":"J. Solid State Chem."},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AWE Johnson","year":"2016","unstructured":"Johnson, A.W.E., et al.: MIMIC-III, a freely accessible critical care database. Scientific Data 3, 160035 (2016)","journal-title":"Scientific Data"},{"key":"34_CR9","doi-asserted-by":"crossref","unstructured":"Liu, L., Cheng, L., Liu, Y., Jia, Y., Rosenblum, D.S.: Recognizing complex activities by a probabilistic interval-based model. In: National Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.10155"},{"key":"34_CR10","doi-asserted-by":"crossref","unstructured":"Marlin, B.M., Kale, D.C., Khemani, R.G., Wetzel, R.C.: Unsupervised pattern discovery in electronic health care data using probabilistic clustering models. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 389\u2013398 (2012)","DOI":"10.1145\/2110363.2110408"},{"issue":"07","key":"34_CR11","doi-asserted-by":"publisher","first-page":"372","DOI":"10.4236\/jsea.2013.67046","volume":"06","author":"L Muflikhah","year":"2013","unstructured":"Muflikhah, L., Wahyuningsih, Y., Nbsp, M.: Fuzzy rule generation for diagnosis of coronary heart disease risk using substractive clustering method. J. Softw. Eng. Appl. 06(07), 372\u2013378 (2013)","journal-title":"J. Softw. Eng. Appl."},{"key":"34_CR12","doi-asserted-by":"crossref","unstructured":"Ni, J., Fei, H., Fan, W., Zhang, X.: Cross-network clustering and cluster ranking for medical diagnosis. In: IEEE International Conference on Data Engineering, pp. 163\u2013166 (2017)","DOI":"10.1109\/ICDE.2017.65"},{"issue":"4","key":"34_CR13","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1109\/69.868904","volume":"12","author":"D Nikovski","year":"2000","unstructured":"Nikovski, D.: Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Trans. Knowl. Data Eng. 12(4), 509\u2013516 (2000)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"34_CR14","unstructured":"Nisha, S., Kathija, A.: Breast cancer data classification using SVM and Naive Bayes techniques. International J. Innov. Res. Comput. Commun. Eng. 4(12) (2016)"},{"key":"34_CR15","unstructured":"Pitman, J.: Combinatorial stochastic processes. Technical report 621, Department of Statistics, UC Berkeley, Lecture notes (2002)"},{"key":"34_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/11527770_4","volume-title":"Artificial Intelligence in Medicine","author":"L Sacchi","year":"2005","unstructured":"Sacchi, L., Bellazzi, R., Larizza, C., Porreca, R., Magni, P.: Learning rules with complex temporal patterns in biomedical domains. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds.) AIME 2005. LNCS (LNAI), vol. 3581, pp. 23\u201332. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11527770_4"},{"issue":"10","key":"34_CR17","doi-asserted-by":"publisher","first-page":"2468","DOI":"10.1109\/TPAMI.2013.33","volume":"35","author":"Y Zhang","year":"2013","unstructured":"Zhang, Y., Zhang, Y., Swears, E., Larios, N., Wang, Z., Ji, Q.: Modeling temporal interactions with interval temporal Bayesian networks for complex activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2468\u20132483 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Knowledge Science, Engineering and Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-99365-2_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:38:46Z","timestamp":1709833126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-99365-2_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319993645","9783319993652"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-99365-2_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 August 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KSEM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Knowledge Science, Engineering and Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changchun","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 August 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 August 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ksem2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ksem2018.venue.link\/","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":"262","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":"62","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":"26","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":"24% - 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":"3.1","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":"10","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)"}},{"value":"We have 3 reviews for 235 submissions, 4 reviews for 25 submissions and 5 review for 2 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}