{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:07:02Z","timestamp":1742911622943,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031453670"},{"type":"electronic","value":"9783031453687"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-45368-7_27","type":"book-chapter","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T20:37:45Z","timestamp":1697056665000},"page":"415-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainability of\u00a0COVID-19 Classification Models Using Dimensionality Reduction of\u00a0SHAP Values"],"prefix":"10.1007","author":[{"given":"Daniel Matheus","family":"Kuhn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melina Silva","family":"de Loreto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariana","family":"Recamonde-Mendoza","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jo\u00e3o Luiz Dihl","family":"Comba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Viviane Pereira","family":"Moreira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","first-page":"103502","DOI":"10.1016\/j.artint.2021.103502","volume":"298","author":"K Aas","year":"2021","unstructured":"Aas, K., Jullum, M., L\u00f8land, A.: Explaining individual predictions when features are dependent: accurate approximations to Shapley values. Artif. Intell. 298, 103502 (2021)","journal-title":"Artif. Intell."},{"key":"27_CR2","doi-asserted-by":"publisher","first-page":"100564","DOI":"10.1016\/j.imu.2021.100564","volume":"24","author":"N Alballa","year":"2021","unstructured":"Alballa, N., Al-Turaiki, I.: Machine learning approaches in Covid-19 diagnosis, mortality, and severity risk prediction: a review. Inf. Med. Unlocked 24, 100564 (2021)","journal-title":"Inf. Med. Unlocked"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, D.C., Veloso, A.A., Borges, K.B.G., das Gra\u00e7as Carvalho, M.: Prognosing the risk of Covid-19 death through a machine learning-based routine blood panel: a retrospective study in brazil. IJMEDI 165, 104835 (2022)","DOI":"10.1016\/j.ijmedinf.2022.104835"},{"issue":"13","key":"27_CR4","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1016\/j.jacc.2021.02.023","volume":"77","author":"CS Broberg","year":"2021","unstructured":"Broberg, C.S., Kovacs, A.H., Sadeghi, S., et al.: Covid-19 in adults with congenital heart disease. JACC 77(13), 1644\u20131655 (2021)","journal-title":"JACC"},{"key":"27_CR5","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.resuscitation.2020.08.124","volume":"156","author":"M Covino","year":"2020","unstructured":"Covino, M., Sandroni, C., Santoro, M., et al.: Predicting intensive care unit admission and death for Covid-19 patients in the emergency department using early warning scores. Resuscitation 156, 84\u201391 (2020)","journal-title":"Resuscitation"},{"issue":"9","key":"27_CR6","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/S2589-7500(20)30186-2","volume":"2","author":"J Futoma","year":"2020","unstructured":"Futoma, J., Simons, M., Panch, T., et al.: The myth of generalisability in clinical research and machine learning in health care. Lancet Digit. Health 2(9), 489\u2013492 (2020)","journal-title":"Lancet Digit. Health"},{"key":"27_CR7","unstructured":"Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Huang, X., Marques-Silva, J.: The inadequacy of Shapley values for explainability. arXiv preprint arXiv:2302.08160 (2023)","DOI":"10.1016\/j.ijar.2023.109112"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"m3339","DOI":"10.1136\/bmj.m3339","volume":"370","author":"SR Knight","year":"2020","unstructured":"Knight, S.R., Ho, A., Pius, R., et al.: Risk stratification of patients admitted to hospital with Covid-19 using the ISARIC WHO clinical characterisation protocol: development and validation of the 4C mortality score. BMJ 370, m3339 (2020)","journal-title":"BMJ"},{"key":"27_CR10","unstructured":"Lundberg, S.M., Erion, G.G., Lee, S.I.: Consistent individualized feature attribution for tree ensembles. arXiv preprint arXiv:1802.03888 (2018)"},{"key":"27_CR11","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"11","key":"27_CR12","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. JMLR 9(11), 2579\u20132605 (2008)","journal-title":"JMLR"},{"key":"27_CR13","unstructured":"Miranda de Paiva, B.B., Delfino-Pereira, P., de Andrade, C.M.V., et al.: Effectiveness, explainability and reliability of machine meta-learning methods for predicting mortality in patients with COVID-19: results of the Brazilian COVID-19 registry. medRxiv (2021)"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, C., Wang, T., Zhang, S.: Cost sensitive classification in data mining. In: Advanced Data Mining and Applications, pp. 1\u201311 (2010)","DOI":"10.1007\/978-3-642-17316-5_1"},{"issue":"11","key":"27_CR15","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1080\/17474086.2020.1831383","volume":"13","author":"M Rostami","year":"2020","unstructured":"Rostami, M., Mansouritorghabeh, H.: D-dimer level in COVID-19 infection: a systematic review. Exp. Rev. Hematol. 13(11), 1265\u20131275 (2020)","journal-title":"Exp. Rev. Hematol."},{"issue":"12","key":"27_CR16","first-page":"e13685","volume":"74","author":"S Subudhi","year":"2020","unstructured":"Subudhi, S., Verma, A., Patel, A.B.: Prognostic machine learning models for Covid-19 to facilitate decision making. IJCP 74(12), e13685 (2020)","journal-title":"IJCP"},{"key":"27_CR17","doi-asserted-by":"publisher","first-page":"m1328","DOI":"10.1136\/bmj.m1328","volume":"369","author":"L Wynants","year":"2020","unstructured":"Wynants, L., Van Calster, B., Collins, G.S., et al.: Prediction models for diagnosis and prognosis of COVID-19: systematic review and critical appraisal. bmj 369, m1328 (2020)","journal-title":"bmj"},{"issue":"10","key":"27_CR18","doi-asserted-by":"publisher","first-page":"E516","DOI":"10.1016\/S2589-7500(20)30217-X","volume":"2","author":"AS Yadaw","year":"2020","unstructured":"Yadaw, A.S., Li, Y., Bose, S., et al.: Clinical features of COVID-19 mortality: development and validation of a clinical prediction model. Lancet Digt. Health 2(10), E516\u2013E525 (2020)","journal-title":"Lancet Digt. Health"},{"key":"27_CR19","unstructured":"Yang, J.: Fast TreeSHAP: accelerating SHAP value computation for trees. arXiv preprint arXiv:2109.09847 (2021)"},{"issue":"7","key":"27_CR20","doi-asserted-by":"publisher","first-page":"e0236618","DOI":"10.1371\/journal.pone.0236618","volume":"15","author":"Z Zhao","year":"2020","unstructured":"Zhao, Z., Chen, A., Hou, W., et al.: Prediction model and risk scores of ICU admission and mortality in COVID-19. PLoS ONE 15(7), e0236618 (2020)","journal-title":"PLoS ONE"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45368-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:31:33Z","timestamp":1709829093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45368-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031453670","9783031453687"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45368-7_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belo Horizonte","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","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":"25 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bracis.dcc.ufmg.br","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"242","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":"90","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":"0","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":"37% - 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":"4","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":"5","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)"}}]}}