{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T20:17:01Z","timestamp":1778789821906,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003107","name":"Bundesministerium f\u00fcr Gesundheit","doi-asserted-by":"publisher","award":["2520DAT66A"],"award-info":[{"award-number":["2520DAT66A"]}],"id":[{"id":"10.13039\/501100003107","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s41060-024-00568-z","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T01:01:53Z","timestamp":1717376513000},"page":"1841-1855","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An attention-based bidirectional LSTM-CNN architecture for the early prediction of sepsis"],"prefix":"10.1007","volume":"20","author":[{"given":"Pronaya Prosun","family":"Das","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lena","family":"Wiese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcel","family":"Mast","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"B\u00f6hnke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antje","family":"Wulff","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Marschollek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louisa","family":"Bode","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henning","family":"Rathert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Jack","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sven","family":"Schamer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Beerbaum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicole","family":"R\u00fcbsamen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e8","family":"Karch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Groszweski-Anders","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Haller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Torsten","family":"Frank","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"568_CR1","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","volume":"89","author":"UR Acharya","year":"2017","unstructured":"Acharya, U.R., Shu Lih, O., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., San, T.R.: A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89, 389\u2013396 (2017)","journal-title":"Comput. Biol. Med."},{"key":"568_CR2","doi-asserted-by":"crossref","unstructured":"Agniel, D., Kohane, I.S., Weber, Griffin\u00a0M.: Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. BMJ, 361 (2018)","DOI":"10.1136\/bmj.k1479"},{"issue":"5","key":"568_CR3","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.resuscitation.2014.01.013","volume":"85","author":"N Alam","year":"2014","unstructured":"Alam, N., Hobbelink, E.L., van Tienhoven, A.-J., van de Ven, P.M., Jansma, E.P., Nanayakkara, P.W.B.: The impact of the use of the early warning score (ews) on patient outcomes: a systematic review. Resuscitation 85(5), 587\u2013594 (2014)","journal-title":"Resuscitation"},{"issue":"6","key":"568_CR4","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1378\/chest.101.6.1644","volume":"101","author":"RC Bone","year":"1992","unstructured":"Bone, R.C., Balk, R.A., Cerra, F.B., Phillip Dellinger, R., Fein, A.M., Knaus, W.A., Schein, R.M.H., Sibbald, W.J.: Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 101(6), 1644\u20131655 (1992)","journal-title":"Chest"},{"key":"568_CR5","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1287\/opre.5.1.63","volume":"5","author":"RG Brown","year":"1957","unstructured":"Brown, R.G.: Exponential smoothing for predicting demand. Operat. Res. 5, 145\u2013145 (1957)","journal-title":"Operat. Res."},{"key":"568_CR6","doi-asserted-by":"crossref","unstructured":"Calvo, M., Jan\u00e9, R.: Sleep stage influence on the autonomic modulation of sleep apnea syndrome. In: 2019 Computing in Cardiology (CinC), 1\u20134. IEEE (2019)","DOI":"10.22489\/CinC.2019.105"},{"key":"568_CR7","unstructured":"cdc.gov. https:\/\/www.cdc.gov\/sepsis\/datareports\/index.html. [Accessed 14-11-2023]"},{"issue":"1","key":"568_CR8","doi-asserted-by":"publisher","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 6085 (2018)","journal-title":"Sci. Rep."},{"key":"568_CR9","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling arXiv preprint (2014) arXiv:1412.3555"},{"key":"568_CR10","unstructured":"Das, P.P., Mast, M., Wiese, L., Jack, T., Wulf, A.: Data extraction for associative classification using mined rules in pediatric intensive care data. BTW 2023 (2023)"},{"issue":"1","key":"568_CR11","doi-asserted-by":"publisher","first-page":"8020","DOI":"10.1038\/s41598-019-44004-w","volume":"9","author":"A Davoudi","year":"2019","unstructured":"Davoudi, A., Malhotra, K.R., Shickel, B., Siegel, S., Williams, S., Ruppert, M., Bihorac, E., Ozrazgat-Baslanti, T., Tighe, P.J., Bihorac, A., et al.: Intelligent ICU for autonomous patient monitoring using pervasive sensing and deep learning. Sci. Rep. 9(1), 8020 (2019)","journal-title":"Sci. Rep."},{"key":"568_CR12","unstructured":"De\u00a0Baets, L., Ruyssinck, J., Peiffer, T., Decruyenaere, J., De\u00a0Turck, F., Ongenae, F., Dhaene, T.: Positive blood culture detection in time series data using a bilstm network (2016) arXiv preprint arXiv:1612.00962"},{"issue":"3","key":"568_CR13","doi-asserted-by":"publisher","first-page":"e5909","DOI":"10.2196\/medinform.5909","volume":"4","author":"T Desautels","year":"2016","unstructured":"Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., Shimabukuro, D., Chettipally, U., Feldman, M.D., Barton, C., et al.: Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med. Inform. 4(3), e5909 (2016)","journal-title":"JMIR Med. Inform."},{"key":"568_CR14","unstructured":"Dmitrievich, I.\u00a0A.: Deep learning in information analysis of electrocardiogram signals for disease diagnostics. The Ministry of Education and Science of The Russian Federation Moscow Institute of Physics and Technology (2015)"},{"issue":"4","key":"568_CR15","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1080\/10903127.2016.1274348","volume":"21","author":"M Dorsett","year":"2017","unstructured":"Dorsett, M., Kroll, M., Smith, C.S., Asaro, P., Liang, S.Y., Moy, H.P.: Gsofa has poor sensitivity for prehospital identification of severe sepsis and septic shock. Prehosp. Emerg. Care 21(4), 489\u2013497 (2017)","journal-title":"Prehosp. Emerg. Care"},{"issue":"10","key":"568_CR16","first-page":"135","volume":"21","author":"MG El-Shafiey","year":"2021","unstructured":"El-Shafiey, M.G., Hagag, A., El-Dahshan, E.-S.A., Ismail, M.A.: A hybrid bidirectional LSTM and 1d CNN for heart disease prediction. IJCSNS 21(10), 135 (2021)","journal-title":"IJCSNS"},{"issue":"4","key":"568_CR17","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1097\/CCM.0000000000002967","volume":"46","author":"M Faisal","year":"2018","unstructured":"Faisal, M., Scally, A., Richardson, D., Beatson, K., Howes, R., Speed, K., Mohammed, M.A.: Development and external validation of an automated computer-aided risk score for predicting sepsis in emergency medical admissions using the patient\u2019s first electronically recorded vital signs and blood test results. Crit. Care Med. 46(4), 612\u2013618 (2018)","journal-title":"Crit. Care Med."},{"issue":"10","key":"568_CR18","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: Continual prediction with lstm. Neural Comput. 12(10), 2451\u20132471 (2000)","journal-title":"Neural Comput."},{"issue":"5\u20136","key":"568_CR19","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Netw. 18(5\u20136), 602\u2013610 (2005)","journal-title":"Neural Netw."},{"issue":"1","key":"568_CR20","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1097\/CCM.0b013e3181b090d0","volume":"38","author":"NA Halpern","year":"2010","unstructured":"Halpern, N.A., Pastores, S.M.: Critical care medicine in the united states 2000\u20132005: an analysis of bed numbers, occupancy rates, payer mix, and costs. Crit. Care Med. 38(1), 65\u201371 (2010)","journal-title":"Crit. Care Med."},{"key":"568_CR21","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s10877-013-9445-6","volume":"27","author":"V Herasevich","year":"2013","unstructured":"Herasevich, V., Kor, D.J., Subramanian, A., Pickering, B.W.: Connecting the dots: rule-based decision support systems in the modern emr era. J. Clin. Monit. Comput. 27, 443\u2013448 (2013)","journal-title":"J. Clin. Monit. Comput."},{"issue":"4","key":"568_CR22","doi-asserted-by":"publisher","first-page":"e0174708","DOI":"10.1371\/journal.pone.0174708","volume":"12","author":"S Horng","year":"2017","unstructured":"Horng, S., Sontag, D.A., Halpern, Y., Jernite, Y., Shapiro, N.I., Nathanson, L.A.: Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE 12(4), e0174708 (2017)","journal-title":"PLoS ONE"},{"key":"568_CR23","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.compbiomed.2017.08.015","volume":"89","author":"JK Hye","year":"2017","unstructured":"Hye, J.K., Ha, Y.K.: Learning representations for the early detection of sepsis with deep neural networks. Comput. Biol. Med. 89, 248\u2013255 (2017)","journal-title":"Comput. Biol. Med."},{"key":"568_CR24","first-page":"866","volume-title":"Moving averages","author":"RJ Hyndman","year":"2011","unstructured":"Hyndman, R.J.: Moving averages, pp. 866\u2013869. Springer, Berlin and Heidelberg (2011)"},{"key":"568_CR25","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, 448\u2013456. pmlr (2015)"},{"key":"568_CR26","doi-asserted-by":"crossref","unstructured":"Jalali, A., Bender, D., Rehman, M., Nadkanri, V., Nataraj, C.: Advanced analytics for outcome prediction in intensive care units. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 2520\u20132524 (2016)","DOI":"10.1109\/EMBC.2016.7591243"},{"key":"568_CR27","unstructured":"Johnson, J., Karpathy, A., Li, F.-F.: CS231n convolutional neural networks for visual recognition. Notes of Stanford CS class (2016). https:\/\/cs231n.stanford.edu\/2016\/"},{"issue":"10","key":"568_CR28","doi-asserted-by":"publisher","first-page":"e495","DOI":"10.1097\/PCC.0000000000001666","volume":"19","author":"R Kamaleswaran","year":"2018","unstructured":"Kamaleswaran, R., Akbilgic, O., Hallman, M.A., West, A.N., Davis, R.L., Shah, S.H.: Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the picu. Pediatr. Crit. Care Med. 19(10), e495\u2013e503 (2018)","journal-title":"Pediatr. Crit. Care Med."},{"issue":"02","key":"568_CR29","doi-asserted-by":"publisher","first-page":"212","DOI":"10.4338\/ACI-2012-12-RA-0053","volume":"4","author":"M Kashiouris","year":"2013","unstructured":"Kashiouris, M., O\u2019Horo, J.C., Pickering, B.W., Herasevich, V.: Diagnostic performance of electronic syndromic surveillance systems in acute care. Appl. Clin. Inform. 4(02), 212\u2013224 (2013)","journal-title":"Appl. Clin. Inform."},{"key":"568_CR30","unstructured":"Kim, Y., Denton, C., Hoang, L., Rush, A.M.: Structured attention networks (2017) arXiv preprint arXiv:1702.00887"},{"key":"568_CR31","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014) arXiv preprint arXiv:1412.6980"},{"key":"568_CR32","doi-asserted-by":"publisher","first-page":"107398","DOI":"10.1016\/j.ymssp.2020.107398","volume":"151","author":"S Kiranyaz","year":"2021","unstructured":"Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1d convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)","journal-title":"Mech. Syst. Signal Process."},{"key":"568_CR33","unstructured":"Koehler, F., Risteski, A.: Representational power of relu networks and polynomial kernels: beyond worst-case analysis (2018) arXiv preprint arXiv:1805.11405"},{"key":"568_CR34","doi-asserted-by":"publisher","first-page":"101820","DOI":"10.1016\/j.artmed.2020.101820","volume":"104","author":"SM Lauritsen","year":"2020","unstructured":"Lauritsen, S.M., Kal\u00f8r, M.E., Kongsgaard, E.L., Lauritsen, K.M., J\u00f8rgensen, M.J., Lange, J., Thiesson, B.: Early detection of sepsis utilizing deep learning on electronic health record event sequences. Artif. Intell. Med. 104, 101820 (2020)","journal-title":"Artif. Intell. Med."},{"issue":"4","key":"568_CR35","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"key":"568_CR36","unstructured":"Lundberg, S., Lee, S.-I.: A unified approach to interpreting model predictions (2017)"},{"issue":"1","key":"568_CR37","doi-asserted-by":"publisher","first-page":"e017833","DOI":"10.1136\/bmjopen-2017-017833","volume":"8","author":"Q Mao","year":"2018","unstructured":"Mao, Q., Jay, M., Hoffman, J.L., Calvert, J., Barton, C., Shimabukuro, D., Shieh, L., Chettipally, U., Fletcher, G., Kerem, Y., et al.: Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and icu. BMJ Open 8(1), e017833 (2018)","journal-title":"BMJ Open"},{"issue":"6","key":"568_CR38","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1586\/eri.12.50","volume":"10","author":"GS Martin","year":"2012","unstructured":"Martin, G.S.: Sepsis, severe sepsis and septic shock: changes in incidence, pathogens and outcomes. Expert Rev. Anti Infect. Ther. 10(6), 701\u2013706 (2012)","journal-title":"Expert Rev. Anti Infect. Ther."},{"issue":"6","key":"568_CR39","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1093\/bib\/bbx044","volume":"19","author":"R Miotto","year":"2018","unstructured":"Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 19(6), 1236\u20131246 (2018)","journal-title":"Brief. Bioinform."},{"key":"568_CR40","doi-asserted-by":"crossref","unstructured":"Mollura, M., Mantoan, G., Romano, S., Lehman, L.-W., Mark, R.G., Barbieri, R.: The role of waveform monitoring in sepsis identification within the first hour of intensive care unit stay. In: 2020 11th conference of the European study group on cardiovascular oscillations (ESGCO). IEEE, pp. 1\u20132(2020)","DOI":"10.1109\/ESGCO49734.2020.9158013"},{"issue":"4","key":"568_CR41","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1097\/CCM.0000000000002936","volume":"46","author":"S Nemati","year":"2018","unstructured":"Nemati, S., Holder, A., Razmi, F., Stanley, M.D., Clifford, G.D., Buchman, T.G.: An interpretable machine learning model for accurate prediction of sepsis in the icu. Crit. Care Med. 46(4), 547 (2018)","journal-title":"Crit. Care Med."},{"issue":"14","key":"568_CR42","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.3390\/app9142921","volume":"9","author":"S Nurmaini","year":"2019","unstructured":"Nurmaini, S., Umi Partan, R., Caesarendra, W., Dewi, T., Naufal Rahmatullah, M., Darmawahyuni, A., Bhayyu, V., Firdaus, F.: An automated ECG beat classification system using deep neural networks with an unsupervised feature extraction technique. Appl. Sci. 9(14), 2921 (2019)","journal-title":"Appl. Sci."},{"issue":"12","key":"568_CR43","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1097\/CCM.0000000000003342","volume":"46","author":"CJ Paoli","year":"2018","unstructured":"Paoli, C.J., Reynolds, M.A., Sinha, M., Gitlin, M., Crouser, E.: Epidemiology and costs of sepsis in the united states-an analysis based on timing of diagnosis and severity level. Crit. Care Med. 46(12), 1889 (2018)","journal-title":"Crit. Care Med."},{"issue":"12","key":"568_CR44","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1109\/TSMC.2017.2705582","volume":"48","author":"B Pourbabaee","year":"2017","unstructured":"Pourbabaee, B., Roshtkhari, M.J., Khorasani, K.: Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients. IEEE Trans. Syst., Man, Cybern.: Syst. 48(12), 2095\u20132104 (2017)","journal-title":"IEEE Trans. Syst., Man, Cybern.: Syst."},{"key":"568_CR45","doi-asserted-by":"crossref","unstructured":"Reyna, Matthew\u00a0A., Josef, C., Seyedi, S., Jeter, R., Shashikumar, S.P., Westover, M.B., Sharma, A., Nemati, S., Clifford, G.D.: Early prediction of sepsis from clinical data: the physionet\/computing in cardiology challenge 2019. In 2019 Computing in Cardiology (CinC), 1. IEEE (2019)","DOI":"10.22489\/CinC.2019.412"},{"issue":"2","key":"568_CR46","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1097\/CCM.0000000000004145","volume":"48","author":"MA Reyna","year":"2020","unstructured":"Reyna, M.A., Josef, C.S., Jeter, R., Shashikumar, S.P., Brandon Westover, M., Nemati, S., Clifford, G.D., Sharma, A.: Early prediction of sepsis from clinical data: the physionet\/computing in cardiology challenge 2019. Crit. Care Med. 48(2), 210\u2013217 (2020)","journal-title":"Crit. Care Med."},{"key":"568_CR47","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \"Why should i trust you?\": explaining the predictions of any classifier (2016)","DOI":"10.18653\/v1\/N16-3020"},{"key":"568_CR48","unstructured":"Ruder, S.: An overview of gradient descent optimization algorithms (2016) arXiv preprint arXiv:1609.04747"},{"key":"568_CR49","doi-asserted-by":"publisher","first-page":"103395","DOI":"10.1016\/j.compbiomed.2019.103395","volume":"113","author":"M Scherpf","year":"2019","unstructured":"Scherpf, M., Gr\u00e4\u00dfer, F., Malberg, H., Zaunseder, S.: Predicting sepsis with a recurrent neural network using the mimic iii database. Comput. Biol. Med. 113, 103395 (2019)","journal-title":"Comput. Biol. Med."},{"issue":"5","key":"568_CR50","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/JBHI.2017.2767063","volume":"22","author":"B Shickel","year":"2017","unstructured":"Shickel, B., Tighe, P.J., Bihorac, A., Rashidi, P.: Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (ehr) analysis. IEEE J. Biomed. Health Inform. 22(5), 1589\u20131604 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8","key":"568_CR51","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1001\/jama.2016.0287","volume":"315","author":"M Singer","year":"2016","unstructured":"Singer, M., Deutschman, C.S., Seymour, C.W., Shankar-Hari, M., Annane, D., Bauer, M., Bellomo, R., Bernard, G.R., Chiche, J.-D., Coopersmith, C.M., et al.: The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8), 801\u2013810 (2016)","journal-title":"JAMA"},{"issue":"4","key":"568_CR52","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)","journal-title":"Resuscitation"},{"issue":"10","key":"568_CR53","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"},{"key":"568_CR54","unstructured":"Torio, C.M., Moore, B.J.: National inpatient hospital costs: the most expensive conditions by payer, 2013: statistical brief# 204. Healthcare cost and utilization project (HCUP) statistical briefs, 2006\u20132016 (2006)"},{"key":"568_CR55","doi-asserted-by":"crossref","unstructured":"Trevethan, R.: Sensitivity, specificity, and predictive values: foundations, pliabilities, and pitfalls in research and practice. Front. Public Health 5, 307 (2017)","DOI":"10.3389\/fpubh.2017.00307"},{"issue":"8","key":"568_CR56","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1016\/j.ajem.2018.10.058","volume":"37","author":"OA Usman","year":"2019","unstructured":"Usman, O.A., Usman, A.A., Ward, M.A.: Comparison of sirs, qsofa, and news for the early identification of sepsis in the emergency department. Am. J. Emerg. Med. 37(8), 1490\u20131497 (2019)","journal-title":"Am. J. Emerg. Med."},{"key":"568_CR57","doi-asserted-by":"crossref","unstructured":"Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., Xu, H.: Time series data augmentation for deep learning: a survey (2020) arXiv preprint arXiv:2002.12478","DOI":"10.24963\/ijcai.2021\/631"},{"key":"568_CR58","doi-asserted-by":"crossref","unstructured":"Yildirim, \u00d6.: A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Comput. Biol. Med. 96, 189\u2013202 (2018)","DOI":"10.1016\/j.compbiomed.2018.03.016"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00568-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41060-024-00568-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-024-00568-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T20:05:32Z","timestamp":1757102732000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41060-024-00568-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":58,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["568"],"URL":"https:\/\/doi.org\/10.1007\/s41060-024-00568-z","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"value":"2364-415X","type":"print"},{"value":"2364-4168","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]},"assertion":[{"value":"30 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}