{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T23:07:55Z","timestamp":1764198475910,"version":"3.46.0"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819549627"},{"type":"electronic","value":"9789819549634"}],"license":[{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T00:00:00Z","timestamp":1763769600000},"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\/978-981-95-4963-4_10","type":"book-chapter","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T17:06:36Z","timestamp":1763744796000},"page":"115-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MediFusion-Flex: An Adaptive Multimodal Deep Learning Framework for\u00a0Clinical Deterioration Prediction in\u00a0Emergency Medicine"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1888-0117","authenticated-orcid":false,"given":"Trong-Nghia","family":"Nguyen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6128-771X","authenticated-orcid":false,"given":"Hong-Hai","family":"Nguyen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-3005","authenticated-orcid":false,"given":"Ngoc Tu","family":"Vu","sequence":"additional","affiliation":[]},{"given":"Tuan Anh","family":"Tran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3575-5035","authenticated-orcid":false,"given":"Soo-Hyung","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Bo-Gun","family":"Kho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6240-2959","authenticated-orcid":false,"given":"Van-Thong","family":"Huynh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,22]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., Murphy, J.R., Boag, W., Weng, W.H., Jin, D., Naumann, T., McDermott, M.: Publicly available clinical bert embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop. pp. 72\u201378 (2019)","DOI":"10.18653\/v1\/W19-1909"},{"issue":"1","key":"10_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"5","key":"10_CR4","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.resuscitation.2012.09.024","volume":"84","author":"MM Churpek","year":"2013","unstructured":"Churpek, M.M., Yuen, T.C., Edelson, D.P.: Predicting clinical deterioration in the hospital: the impact of outcome selection. Resuscitation 84(5), 564\u2013568 (2013)","journal-title":"Resuscitation"},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1097\/CCM.0000000000001571","volume":"44","author":"MM Churpek","year":"2016","unstructured":"Churpek, M.M., Yuen, T.C., Winslow, C., Meltzer, D.O., Kattan, M.W., Edelson, D.P.: Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit. Care Med. 44(2), 368\u2013374 (2016). https:\/\/doi.org\/10.1097\/CCM.0000000000001571","journal-title":"Crit. Care Med."},{"issue":"3","key":"10_CR6","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1023\/A:1022627411411","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273\u2013297 (1995)","journal-title":"Mach. Learn."},{"issue":"1","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3458754","volume":"3","author":"Y Gu","year":"2021","unstructured":"Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., Poon, H.: Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare 3(1), 1\u201323 (2021)","journal-title":"ACM Transactions on Computing for Healthcare"},{"key":"10_CR8","unstructured":"He, P., Liu, X., Gao, J., Chen, W.: Deberta: Decoding-enhanced bert with disentangled attention. arXiv preprint arXiv:2006.03654 (2020)"},{"issue":"8","key":"10_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Hosmer, D., Lemeshow, S., Sturdivant, R.: Applied Logistic Regression. John Wiley & Sons, 3rd edn. (2013)","DOI":"10.1002\/9781118548387"},{"issue":"9477","key":"10_CR11","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1016\/S0140-6736(05)66733-5","volume":"365","author":"MS Investigators","year":"2005","unstructured":"Investigators, M.S., et al.: Introduction of the medical emergency team (met) system: a cluster-randomised controlled trial. The Lancet 365(9477), 2091\u20132097 (2005). https:\/\/doi.org\/10.1016\/S0140-6736(05)66733-5","journal-title":"The Lancet"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., Pollard, T.J., Shen, L., Lehman, L.w.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony\u00a0Celi, L., Mark, R.G.: Mimic-iii, a freely accessible critical care database. Scientific data 3(1), 1\u20139 (2016)","DOI":"10.1038\/sdata.2016.35"},{"issue":"2","key":"10_CR13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211057","volume":"14","author":"DA Kaji","year":"2019","unstructured":"Kaji, D.A., Zech, J.R., Kim, J.S., Cho, S.K., Dangayach, N.S., Costa, A.B., Oermann, E.K.: An attention based deep learning model of clinical events in the intensive care unit. PLoS ONE 14(2), e0211057 (2019)","journal-title":"PLoS ONE"},{"issue":"1","key":"10_CR14","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1186\/s13054-020-2752-7","volume":"24","author":"MW Kang","year":"2020","unstructured":"Kang, M.W., Kim, J., Kim, D.K., Oh, K.H., Joo, K.W., Kim, Y.S., Han, S.S.: Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy. Crit. Care 24(1), 42 (2020). https:\/\/doi.org\/10.1186\/s13054-020-2752-7","journal-title":"Crit. Care"},{"issue":"3","key":"10_CR15","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/j.resuscitation.2004.05.016","volume":"62","author":"J Kause","year":"2004","unstructured":"Kause, J., Smith, G., Prytherch, D., Parr, M., Flabouris, A., Hillman, K., et al.: A comparison of antecedents to cardiac arrests, deaths and emergency intensive care admissions in australia and new zealand, and the united kingdom the academia study. Resuscitation 62(3), 275\u2013282 (2004). https:\/\/doi.org\/10.1016\/j.resuscitation.2004.05.016","journal-title":"Resuscitation"},{"issue":"11","key":"10_CR16","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"issue":"4","key":"10_CR17","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., Kang, J.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"key":"10_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106975","volume":"100","author":"TN Nguyen","year":"2025","unstructured":"Nguyen, T.N., Kim, S.H., Kho, B.G., Do, N.T., Iyortsuun, N.K., Lee, G.S., Yang, H.J.: Temporal variational autoencoder model for in-hospital clinical emergency prediction. Biomed. Signal Process. Control 100, 106975 (2025)","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"10_CR19","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/MIS.2024.3408290","volume":"39","author":"TN Nguyen","year":"2024","unstructured":"Nguyen, T.N., Kim, S.H., Kho, B.G., Yang, H.J.: Multigradient siamese temporal model for the prediction of clinical events in rapid response systems. IEEE Intell. Syst. 39(6), 58\u201369 (2024). https:\/\/doi.org\/10.1109\/MIS.2024.3408290","journal-title":"IEEE Intell. Syst."},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Peng, Y., Yan, S., Lu, Z.: Transfer learning in biomedical natural language processing: An evaluation of bert and elmo on ten benchmarking datasets. In: Proceedings of the BioNLP 2019 workshop. pp. 58\u201365 (2019)","DOI":"10.18653\/v1\/W19-5006"},{"key":"10_CR21","volume-title":"National early warning score (news) 2: Standardising the assessment of acute-illness severity in the nhs","author":"RC Physicians","year":"2017","unstructured":"Physicians, R.C.: National early warning score (news) 2: Standardising the assessment of acute-illness severity in the nhs. Report, RCP, London (2017)"},{"issue":"1","key":"10_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.178","volume":"5","author":"TJ Pollard","year":"2018","unstructured":"Pollard, T.J., Johnson, A.E., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eicu collaborative research database, a freely available multi-center database for critical care research. Scientific data 5(1), 1\u201313 (2018)","journal-title":"Scientific data"},{"issue":"1","key":"10_CR23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., Sun, M., et al.: Scalable and accurate deep learning with electronic health records. NPJ digital medicine 1(1), 18 (2018)","journal-title":"NPJ digital medicine"},{"issue":"4","key":"10_CR24","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.resuscitation.2012.12.016","volume":"84","author":"GB Smith","year":"2013","unstructured":"Smith, G.B., Prytherch, D.R., Meredith, P., Schmidt, P.E., Featherstone, P.I.: The ability of the national early warning score (news) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 84(4), 465\u2013470 (2013). https:\/\/doi.org\/10.1016\/j.resuscitation.2012.12.016","journal-title":"Resuscitation"},{"issue":"10","key":"10_CR25","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1093\/qjmed\/94.10.521","volume":"94","author":"CP Subbe","year":"2001","unstructured":"Subbe, C.P., Kruger, M., Rutherford, P., Gemmel, L.: Validation of a modified early warning score in medical admissions. QJM 94(10), 521\u2013526 (2001)","journal-title":"QJM"},{"issue":"7767","key":"10_CR26","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","volume":"572","author":"N Toma\u0161ev","year":"2019","unstructured":"Toma\u0161ev, N., Glorot, X., Rae, J.W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al.: A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572(7767), 116\u2013119 (2019)","journal-title":"Nature"},{"key":"10_CR27","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems. pp. 5998\u20136008 (2017)"}],"container-title":["Lecture Notes in Computer Science","Multi-disciplinary Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4963-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T23:03:39Z","timestamp":1764198219000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4963-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,22]]},"ISBN":["9789819549627","9789819549634"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4963-4_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,11,22]]},"assertion":[{"value":"22 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIWAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multi-disciplinary Trends in Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"3 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miwai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miwai25.miwai.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}