{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T23:49:32Z","timestamp":1776901772519,"version":"3.51.2"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032167071","type":"print"},{"value":"9783032167088","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-16708-8_9","type":"book-chapter","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T23:19:58Z","timestamp":1776899998000},"page":"105-119","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explaining Medical Time Series Classification Through Boundary-Aware Feature Analysis"],"prefix":"10.1007","author":[{"given":"Christel","family":"Sirocchi","sequence":"first","affiliation":[]},{"given":"Damiano","family":"Verda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/s10618-018-0596-4","volume":"33","author":"A Abanda","year":"2019","unstructured":"Abanda, A., Mori, U., Lozano, J.A.: A review on distance based time series classification. Data Min. Knowl. Disc. 33(2), 378\u2013412 (2019)","journal-title":"Data Min. Knowl. Disc."},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1007\/s10618-015-0425-y","volume":"30","author":"MG Baydogan","year":"2016","unstructured":"Baydogan, M.G., Runger, G.: Time series representation and similarity based on local autopatterns. Data Min. Knowl. Disc. 30, 476\u2013509 (2016)","journal-title":"Data Min. Knowl. Disc."},{"issue":"6","key":"9_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3645103","volume":"56","author":"G Ciatto","year":"2024","unstructured":"Ciatto, G., Sabbatini, F., Agiollo, A., Magnini, M., Omicini, A.: Symbolic knowledge extraction and injection with sub-symbolic predictors: a systematic literature review. ACM Comput. Surv. 56(6), 1\u201335 (2024)","journal-title":"ACM Comput. Surv."},{"key":"9_CR4","unstructured":"Ghahremani, Y., Metsis, V.: Time series embedding methods for classification tasks: a review. arXiv preprint arXiv:2501.13392 (2025)"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215\u2013e220 (2000)","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data? In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems. vol.\u00a035, pp. 507\u2013520. Curran Associates, Inc. (2022)","DOI":"10.52202\/068431-0037"},{"key":"9_CR7","unstructured":"Johnson, A.E., Kramer, A.A., Clifford, G.D.: Data preprocessing and mortality prediction: the physionet\/cinc 2012 challenge revisited. In: Computing in Cardiology 2014. pp. 157\u2013160. IEEE (2014)"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: Mimic-iii, a freely accessible critical care database. Sci. Data 3(1), 1\u20139 (2016)","DOI":"10.1038\/sdata.2016.35"},{"key":"9_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102676","volume":"145","author":"F Leiser","year":"2023","unstructured":"Leiser, F., Rank, S., Schmidt-Kraepelin, M., Thiebes, S., Sunyaev, A.: Medical informed machine learning: a scoping review and future research directions. Artif. Intell. Med. 145, 102676 (2023)","journal-title":"Artif. Intell. Med."},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.inffus.2020.09.006","volume":"66","author":"F Piccialli","year":"2021","unstructured":"Piccialli, F., Di Somma, V., Giampaolo, F., Cuomo, S., Fortino, G.: A survey on deep learning in medicine: why, how and when? Information Fusion 66, 111\u2013137 (2021)","journal-title":"Information Fusion"},{"key":"9_CR11","unstructured":"Shukla, S.N., Marlin, B.: Interpolation-prediction networks for irregularly sampled time series. In: International Conference on Learning Representations (2019)"},{"key":"9_CR12","unstructured":"Shukla, S.N., Marlin, B.: Multi-time attention networks for irregularly sampled time series. In: International Conference on Learning Representations (2021)"},{"issue":"Suppl 4","key":"9_CR13","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1186\/s12911-024-02582-4","volume":"24","author":"C Sirocchi","year":"2024","unstructured":"Sirocchi, C., Bogliolo, A., Montagna, S.: Medical-informed machine learning: integrating prior knowledge into medical decision systems. BMC Med. Inform. Decis. Mak. 24(Suppl 4), 186 (2024)","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"2","key":"9_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3624480","volume":"18","author":"F Spinnato","year":"2023","unstructured":"Spinnato, F., Guidotti, R., Monreale, A., Nanni, M., Pedreschi, D., Giannotti, F.: Understanding any time series classifier with a subsequence-based explainer. ACM Trans. Knowl. Discov. Data 18(2), 1\u201334 (2023)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"issue":"1","key":"9_CR15","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/s41746-020-0221-y","volume":"3","author":"RT Sutton","year":"2020","unstructured":"Sutton, R.T., Pincock, D., Baumgart, D.C., Sadowski, D.C., Fedorak, R.N., Kroeker, K.I.: An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Med. 3(1), 17 (2020)","journal-title":"NPJ Digital Med."},{"issue":"6","key":"9_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3516367","volume":"16","author":"S Tipirneni","year":"2022","unstructured":"Tipirneni, S., Reddy, C.K.: Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series. ACM Trans. Knowl. Discov. Data (TKDD) 16(6), 1\u201317 (2022)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"issue":"6","key":"9_CR17","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/s41551-023-01045-x","volume":"7","author":"HY Zhou","year":"2023","unstructured":"Zhou, H.Y., et al.: A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nat. Biomed. Eng. 7(6), 743\u2013755 (2023)","journal-title":"Nat. Biomed. Eng."}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16708-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T23:20:00Z","timestamp":1776900000000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16708-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032167071","9783032167088"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16708-8_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HC_AIxIA_HYDRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Workshop on Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bologna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hc_aixia_hydra2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/unical.it\/hcaixia-hydra-2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}