{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T18:40:54Z","timestamp":1743014454545,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031343438"},{"type":"electronic","value":"9783031343445"}],"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\/978-3-031-34344-5_2","type":"book-chapter","created":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T23:03:59Z","timestamp":1685919839000},"page":"13-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Boosted Random Forests for\u00a0Predicting Treatment Failure of\u00a0Chemotherapy Regimens"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4293-2979","authenticated-orcid":false,"given":"Muhammad Usamah","family":"Shahid","sequence":"first","affiliation":[]},{"given":"Muddassar","family":"Farooq","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,5]]},"reference":[{"issue":"2","key":"2_CR1","doi-asserted-by":"publisher","first-page":"823","DOI":"10.3390\/app11020823","volume":"11","author":"F Arezzo","year":"2021","unstructured":"Arezzo, F., et al.: A machine learning tool to predict the response to neoadjuvant chemotherapy in patients with locally advanced cervical cancer. Appl. Sci. 11(2), 823 (2021)","journal-title":"Appl. Sci."},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/978-3-030-59137-3_14","volume-title":"Artificial Intelligence in Medicine","author":"F Jabbari","year":"2020","unstructured":"Jabbari, F., Villaruz, L.C., Davis, M., Cooper, G.F.: Lung cancer survival prediction using instance-specific Bayesian networks. In: Michalowski, M., Moskovitch, R. (eds.) AIME 2020. LNCS (LNAI), vol. 12299, pp. 149\u2013159. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59137-3_14"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Elixhauser, A., Steiner, C., Harris, D.R., Coffey, R.M.: Comorbidity measures for use with administrative data. Medical Care, pp. 8\u201327 (1998)","DOI":"10.1097\/00005650-199801000-00004"},{"key":"2_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/978-3-030-21642-9_42","volume-title":"Artificial Intelligence in Medicine","author":"J French","year":"2019","unstructured":"French, J., et al.: Identification of patient prescribing predicting cancer diagnosis using boosted decision trees. In: Ria\u00f1o, D., Wilk, S., ten Teije, A. (eds.) AIME 2019. LNCS (LNAI), vol. 11526, pp. 328\u2013333. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21642-9_42"},{"key":"2_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100399","volume":"20","author":"A Kashef","year":"2020","unstructured":"Kashef, A., Khatibi, T., Mehrvar, A.: Treatment outcome classification of pediatric acute lymphoblastic leukemia patients with clinical and medical data using machine learning: a case study at Mahak hospital. Inform. Med. Unlocked 20, 100399 (2020)","journal-title":"Inform. Med. Unlocked"},{"key":"2_CR6","unstructured":"Kluwer, W.: Medi-span Generic Product Identifier (GPI) (2019)"},{"key":"2_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/978-3-030-21642-9_17","volume-title":"Artificial Intelligence in Medicine","author":"S Malakouti","year":"2019","unstructured":"Malakouti, S., Hauskrecht, M.: Predicting patient\u2019s diagnoses and diagnostic categories from clinical-events in EHR data. In: Ria\u00f1o, D., Wilk, S., ten Teije, A. (eds.) AIME 2019. LNCS (LNAI), vol. 11526, pp. 125\u2013130. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21642-9_17"},{"issue":"9","key":"2_CR8","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1587\/transinf.2014OPP0004","volume":"98","author":"Y Mishina","year":"2015","unstructured":"Mishina, Y., Murata, R., Yamauchi, Y., Yamashita, T., Fujiyoshi, H.: Boosted random forest. IEICE Trans. Inf. Syst. 98(9), 1630\u20131636 (2015)","journal-title":"IEICE Trans. Inf. Syst."},{"issue":"1","key":"2_CR9","doi-asserted-by":"publisher","first-page":"10936","DOI":"10.1038\/s41598-020-67823-8","volume":"10","author":"H Moghadas-Dastjerdi","year":"2020","unstructured":"Moghadas-Dastjerdi, H., Sha-E-Tallat, H.R., Sannachi, L., Sadeghi-Naini, A., Czarnota, G.J.: A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning. Sci. Rep. 10(1), 10936 (2020)","journal-title":"Sci. Rep."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Ribba, B., et al.: A review of mixed-effects models of tumor growth and effects of anticancer drug treatment used in population analysis. CPT: Pharmacometr. Syst. Pharmacol. 3(5), 1\u201310 (2014)","DOI":"10.1038\/psp.2014.12"},{"key":"2_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/978-3-030-21642-9_24","volume-title":"Artificial Intelligence in Medicine","author":"A Silvina","year":"2019","unstructured":"Silvina, A., Bowles, J., Hall, P.: On predicting the outcomes of chemotherapy treatments in breast cancer. In: Ria\u00f1o, D., Wilk, S., ten Teije, A. (eds.) AIME 2019. LNCS (LNAI), vol. 11526, pp. 180\u2013190. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-21642-9_24"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-34344-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:40:03Z","timestamp":1709818803000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-34344-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031343438","9783031343445"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-34344-5_2","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":"5 June 2023","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":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portoroz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovenia","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":"12 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.aimedicine.info\/aime23\/","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":"EASY CHAIR","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"108","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":"23","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":"24","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":"21% - 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 meta-review)","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)"}},{"value":"3 (demonstration papers, similar to short papers)","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)"}}]}}