{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:30:27Z","timestamp":1742913027665,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954048"},{"type":"electronic","value":"9783030954055"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-95405-5_6","type":"book-chapter","created":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T19:03:13Z","timestamp":1643655793000},"page":"73-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Interpretable Machine Learning Approach for Predicting Hospital Length of Stay and Readmission"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1265-7926","authenticated-orcid":false,"given":"Yuxi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3591-4959","authenticated-orcid":false,"given":"Shaowen","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,31]]},"reference":[{"issue":"8","key":"6_CR1","doi-asserted-by":"publisher","first-page":"e0203316","DOI":"10.1371\/journal.pone.0203316","volume":"13","author":"C Morley","year":"2018","unstructured":"Morley, C., Unwin, M., Peterson, G.M., Stankovich, J., Kinsman, L.: Emergency department crowding: a systematic review of causes, consequences and solutions. PloS one 13(8), e0203316 (2018)","journal-title":"PloS one"},{"issue":"3","key":"6_CR2","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1016\/j.jinf.2011.12.007","volume":"64","author":"S Jo","year":"2012","unstructured":"Jo, S., et al.: Emergency department crowding is associated with 28-day mortality in community-acquired pneumonia patients. J. Infect. 64(3), 268\u2013275 (2012)","journal-title":"J. Infect."},{"issue":"1","key":"6_CR3","doi-asserted-by":"publisher","first-page":"e0165756","DOI":"10.1371\/journal.pone.0165756","volume":"12","author":"CH Chaou","year":"2017","unstructured":"Chaou, C.H., et al.: Predicting length of stay among patients discharged from the emergency department-using an accelerated failure time model. PloS one 12(1), e0165756 (2017)","journal-title":"PloS one"},{"issue":"4","key":"6_CR4","doi-asserted-by":"publisher","first-page":"e0195901","DOI":"10.1371\/journal.pone.0195901","volume":"13","author":"H Baek","year":"2018","unstructured":"Baek, H., Cho, M., Kim, S., Hwang, H., Song, M., Yoo, S.: Analysis of length of hospital stay using electronic health records: a statistical and data mining approach. PloS one 13(4), e0195901 (2018)","journal-title":"PloS one"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Upadhyay, S., Stephenson, A.L., Smith, D.G.: Readmission rates and their impact on hospital financial performance: a study of Washington hospitals. INQUIRY J. Health Care Organ. Provision Finan. 56, 0046958019860386 (2019)","DOI":"10.1177\/0046958019860386"},{"key":"6_CR6","unstructured":"Authority, N.H.P.: Hospital performance: length of stay in public hospitals in 2011\u201312 (2013)"},{"key":"6_CR7","unstructured":"CMS: Hospital readmissions reduction program (HRRP). https:\/\/www.cms.gov\/Medicare\/Medicare-Fee-for-Service-Payment\/AcuteInpatientPPS\/Readmissions-Reduction-Program, Accessed 4 July 2021"},{"issue":"1","key":"6_CR8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1093\/cid\/cix731","volume":"66","author":"J Wiens","year":"2017","unstructured":"Wiens, J., Shenoy, E.S.: Machine learning for healthcare: on the verge of a major shift in healthcare epidemiology. Clin. Infect. Dis. 66(1), 149\u2013153 (2017). https:\/\/doi.org\/10.1093\/cid\/cix731","journal-title":"Clin. Infect. Dis."},{"issue":"9","key":"6_CR9","doi-asserted-by":"publisher","first-page":"E1045","DOI":"10.1111\/j.1553-2712.2012.01435.x","volume":"19","author":"JS Peck","year":"2012","unstructured":"Peck, J.S., Benneyan, J.C., Nightingale, D.J., Gaehde, S.A.: Predicting emergency department inpatient admissions to improve same-day patient flow. Acad. Emerg. Med. 19(9), E1045\u2013E1054 (2012)","journal-title":"Acad. Emerg. Med."},{"key":"6_CR10","unstructured":"Combes, C., Kadri, F., Chaabane, S.: Predicting hospital length of stay using regression models: application to emergency department. In: 10\u00e8me Conf\u00e9rence Francophone de Mod\u00e9lisation, Optimisation et Simulation-MOSIM\u201914 (2014)"},{"issue":"8","key":"6_CR11","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1111\/j.1553-2712.2011.01125.x","volume":"18","author":"Y Sun","year":"2011","unstructured":"Sun, Y., Heng, B.H., Tay, S.Y., Seow, E.: Predicting hospital admissions at emergency department triage using routine administrative data. Acad. Emerg. Med. 18(8), 844\u2013850 (2011)","journal-title":"Acad. Emerg. Med."},{"key":"6_CR12","unstructured":"Leegon, J., Jones, I., Lanaghan, K., Aronsky, D.: Predicting hospital admission for emergency department patients using a bayesian network. In: AMIA Annual Symposium Proceedings, vol. 2005, p. 1022. American Medical Informatics Association (2005)"},{"issue":"1","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0211-0","volume":"3","author":"CB Hilton","year":"2020","unstructured":"Hilton, C.B., et al.: Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence. NPJ Dig. Med. 3(1), 1\u20138 (2020)","journal-title":"NPJ Dig. Med."},{"key":"6_CR14","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-47364-2_1","volume-title":"International Joint Conference SOCO\u201916-CISIS\u201916-ICEUTE\u201916","author":"A Artetxe","year":"2017","unstructured":"Artetxe, A., Beristain, A., Gra\u00f1a, M., Besga, A.: Predicting 30-day emergency readmission risk. In: Gra\u00f1a, M., L\u00f3pez-Guede, J.M., Etxaniz, O., Herrero, \u00c1., Quinti\u00e1n, H., Corchado, E. (eds.) SOCO\/CISIS\/ICEUTE -2016. AISC, vol. 527, pp. 3\u201312. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-47364-2_1"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Baig, M.M., et al.: Machine learning-based risk of hospital readmissions: predicting acute readmissions within 30 days of discharge. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2178\u20132181. IEEE (2019)","DOI":"10.1109\/EMBC.2019.8856646"},{"key":"6_CR16","doi-asserted-by":"publisher","first-page":"104136","DOI":"10.1016\/j.ijmedinf.2020.104136","volume":"139","author":"D Morel","year":"2020","unstructured":"Morel, D., Kalvin, C.Y., Liu-Ferrara, A., Caceres-Suriel, A.J., Kurtz, S.G., Tabak, Y.P.: Predicting hospital readmission in patients with mental or substance use disorders: a machine learning approach. Int. J. Med. Inf. 139, 104136 (2020)","journal-title":"Int. J. Med. Inf."},{"key":"6_CR17","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.neunet.2020.03.012","volume":"126","author":"BP Roquette","year":"2020","unstructured":"Roquette, B.P., Nagano, H., Marujo, E.C., Maiorano, A.C.: Prediction of admission in pediatric emergency department with deep neural networks and triage textual data. Neural Netw. 126, 170\u2013177 (2020)","journal-title":"Neural Netw."},{"issue":"7","key":"6_CR18","doi-asserted-by":"publisher","first-page":"e0201016","DOI":"10.1371\/journal.pone.0201016","volume":"13","author":"WS Hong","year":"2018","unstructured":"Hong, W.S., Haimovich, A.D., Taylor, R.A.: Predicting hospital admission at emergency department triage using machine learning. PloS one 13(7), e0201016 (2018)","journal-title":"PloS one"},{"key":"6_CR19","unstructured":"Dorogush, A.V., Ershov, V., Gulin, A.: Catboost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363 (2018)"},{"issue":"1","key":"6_CR20","first-page":"2522","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S.M., et al.: From local explanations to global understanding with explainable AI for trees. Nature Mach. Intell. 2(1), 2522\u20135839 (2020)","journal-title":"Nature Mach. Intell."},{"issue":"1","key":"6_CR21","first-page":"559","volume":"18","author":"G Lema\u00eetre","year":"2017","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(1), 559\u2013563 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR22","unstructured":"Authority, I.H.P.: Australian refined diagnosis related groups version 6.x addendum, https:\/\/www.ihpa.gov.au\/publications\/australian-refined-diagnosis-related-groups-version-6x-addendum, Accessed 10 July 2021"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Pereira, M., Singh, V., Hon, C.P., McKelvey, T.G., Sushmita, S., De Cock, M.: Predicting future frequent users of emergency departments in California state. In: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 603\u2013610 (2016)","DOI":"10.1145\/2975167.2985845"},{"issue":"Mar","key":"6_CR24","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157\u20131182 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Bergstra, J., Yamins, D., Cox, D.D., et al.: Hyperopt: a python library for optimizing the hyperparameters of machine learning algorithms. In: Proceedings of the 12th Python in Science Conference, vol. 13, p. 20. Citeseer (2013)","DOI":"10.25080\/Majora-8b375195-003"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95405-5_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T19:03:42Z","timestamp":1643655822000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95405-5_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954048","9783030954055"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95405-5_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"31 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 February 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 February 2022","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":"adma2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adma2021.net\/","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":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"116","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":"26","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":"35","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":"22% - 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":"5","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}