{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:53:20Z","timestamp":1781675600563,"version":"3.54.5"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032136534","type":"print"},{"value":"9783032136541","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\/978-3-032-13654-1_24","type":"book-chapter","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:12:08Z","timestamp":1781673128000},"page":"241-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SepsiGraph: A Graph-Based Multimodal Approach for\u00a0Early Sepsis Prediction in\u00a0Dynamic Resource-Constrained Clinical Settings"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5505-0855","authenticated-orcid":false,"given":"Sofia","family":"Bourhim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oumayma","family":"Bourhim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,2]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Seymour, C., et al.: Time to treatment and mortality during mandated emergency care for sepsis. N. Engl. J. Med. 376, 2235\u20132244 (2017)","DOI":"10.1056\/NEJMoa1703058"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Kumar, A., et al.: Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med. 34, 1589\u20131596 (2006)","DOI":"10.1097\/01.CCM.0000217961.75225.E9"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Singer, M., et al.: The third international consensus definitions for sepsis and septic shock (Sepsis-3). Jama. 315, 801\u2013810 (2016)","DOI":"10.1001\/jama.2016.0287"},{"key":"24_CR4","unstructured":"Ho, J., Lee, C., Ghosh, J.: Imputation-enhanced prediction of septic shock in ICU patients. In: Proceedings of The ACM SIGKDD Workshop On Health Informatics (HI-KDD12), pp. 18 (2012)"},{"key":"24_CR5","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1109\/JBHI.2021.3092835","volume":"26","author":"Z Wang","year":"2021","unstructured":"Wang, Z., Yao, B.: Multi-branching temporal convolutional network for sepsis prediction. IEEE J. Biomed. Health Inf. 26, 876\u2013887 (2021)","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"He, Z., et al.: Early sepsis prediction using ensemble learning with features extracted from LSTM recurrent neural network. In: 2019 Computing In Cardiology (CinC), p. 1 (2019)","DOI":"10.22489\/CinC.2019.269"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Johnson, A., et al.: MIMIC-IV, a freely accessible electronic health record dataset. Sci. Data 10, 1 (2023)","DOI":"10.1038\/s41597-022-01899-x"},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Johnson, A., et al.: MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6, 317 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"24_CR9","first-page":"1297","volume":"9471","author":"D Ronaldo","year":"2025","unstructured":"Ronaldo, D.: Real-time sepsis prediction in intensive care units using temporal deep learning models on longitudinal electronic health records. J. ID 9471, 1297 (2025)","journal-title":"J. ID"},{"key":"24_CR10","unstructured":"Yin, C., et al.: SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction. ArXiv Preprint arXiv:2501.00190 (2024)"},{"key":"24_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cca.2023.117738","volume":"553","author":"L Agnello","year":"2024","unstructured":"Agnello, L., Vidali, M., Padoan, A., Lucis, R., Mancini, A., Guerranti, R., Plebani, M., Ciaccio, M., Carobene, A.: Machine learning algorithms in sepsis. Clin. Chim. Acta 553, 117738 (2024)","journal-title":"Clin. Chim. Acta"},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Ghanvatkar, S., Rajan, V.: Graph-Based Patient Representation for Multimodal Clinical Data: Addressing Data Heterogeneity. MedRxiv, pp. 2023\u201312 (2023)","DOI":"10.1101\/2023.12.07.23299673"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing in Resource Constrained Settings"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-13654-1_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:12:19Z","timestamp":1781673139000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-13654-1_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032136534","9783032136541"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-13654-1_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MIRASOL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Medical Image Computing in Resource Constrained Settings","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 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":"mirasol2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/event.fourwaves.com\/mirasol\/pages","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}