{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T14:50:23Z","timestamp":1774450223844,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T00:00:00Z","timestamp":1595203200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R&D Program of China","award":["2017YFC0806402, 2017YFC0806404, 2017YFC0806406"],"award-info":[{"award-number":["2017YFC0806402, 2017YFC0806404, 2017YFC0806406"]}]},{"name":"Science and Technology Program of Tianjin, China","award":["18ZXJMTG00060"],"award-info":[{"award-number":["18ZXJMTG00060"]}]},{"name":"logistics scientific research foundation program at the Military Medical Innovation Project","award":["16CXZ034"],"award-info":[{"award-number":["16CXZ034"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s11517-020-02174-0","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T09:03:01Z","timestamp":1595235781000},"page":"2195-2238","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A machine learning approach for mortality prediction only using non-invasive parameters"],"prefix":"10.1007","volume":"58","author":[{"given":"Guang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"JiaMeng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"2174_CR1","unstructured":"Le GJ, Lemeshow S, Saulnier F (1993) A new simplified acute physiology score (SAPS II) based on a European\/North American multicenter study. J Am Med Assoc"},{"key":"2174_CR2","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1097\/01.CCM.0000178178.02574.AB","volume":"33","author":"DGT Arts","year":"2005","unstructured":"Arts DGT, de Keizer NF, Vroom MB, de Jonge E (2005) Reliability and accuracy of sequential organ failure assessment (SOFA) scoring. Crit Care Med 33:1988. \nhttps:\/\/doi.org\/10.1097\/01.CCM.0000178178.02574.AB","journal-title":"Crit Care Med"},{"key":"2174_CR3","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/S0022-3476(97)70065-9","volume":"131","author":"MM Pollack","year":"1997","unstructured":"Pollack MM, Patel KM, Ruttimann UE (1997) The pediatric risk of mortality III\u2014 acute physiology score (PRISM III-APS): a method of assessing physiologic instability for pediatric intensive care unit patients. J Pediatr 131:575\u2013581. \nhttps:\/\/doi.org\/10.1016\/S0022-3476(97)70065-9","journal-title":"J Pediatr"},{"key":"2174_CR4","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1111\/j.1399-6576.2008.01586.x","volume":"52","author":"K Strand","year":"2008","unstructured":"Strand K, Flaatten H (2008) Severity scoring in the ICU: a review: severity scoring in the ICU. Acta Anaesthesiol Scand 52:467\u2013478. \nhttps:\/\/doi.org\/10.1111\/j.1399-6576.2008.01586.x","journal-title":"Acta Anaesthesiol Scand"},{"key":"2174_CR5","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1097\/00075198-200112000-00018","volume":"7","author":"R Moreno","year":"2001","unstructured":"Moreno R, Matos R (2001) New issues in severity scoring: interfacing the ICU and evaluating it. Curr Opin Crit Care 7:469\u2013474. \nhttps:\/\/doi.org\/10.1097\/00075198-200112000-00018","journal-title":"Curr Opin Crit Care"},{"key":"2174_CR6","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1097\/CCM.0b013e3182772adb","volume":"41","author":"L Mayaud","year":"2013","unstructured":"Mayaud L, Lai PS, Clifford GD et al (2013) Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension*. Crit Care Med 41:954\u2013962. \nhttps:\/\/doi.org\/10.1097\/CCM.0b013e3182772adb","journal-title":"Crit Care Med"},{"key":"2174_CR7","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.jbi.2018.02.008","volume":"79","author":"R Davoodi","year":"2018","unstructured":"Davoodi R, Moradi MH (2018) Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. J Biomed Inform 79:48\u201359. \nhttps:\/\/doi.org\/10.1016\/j.jbi.2018.02.008","journal-title":"J Biomed Inform"},{"key":"2174_CR8","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s11517-013-1091-0","volume":"51","author":"KA Kim","year":"2013","unstructured":"Kim KA, Choi JY, Yoo TK (2013) Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Med Biol Eng Comput 51:1059\u20131067. \nhttps:\/\/doi.org\/10.1007\/s11517-013-1091-0","journal-title":"Med Biol Eng Comput"},{"key":"2174_CR9","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.aei.2006.12.002","volume":"21","author":"J Ramon","year":"2007","unstructured":"Ramon J, Fierens D, G\u00fciza F et al (2007) Mining data from intensive care patients. Adv Eng Inform 21:243\u2013256. \nhttps:\/\/doi.org\/10.1016\/j.aei.2006.12.002","journal-title":"Adv Eng Inform"},{"key":"2174_CR10","unstructured":"Lee CH, Arzeno NM, Ho JC et al (2012) An imputation-enhanced algorithm for ICU mortality prediction. Comput Cardiol:253\u2013256"},{"key":"2174_CR11","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1097\/CCM.0000000000003148","volume":"46","author":"GE Weissman","year":"2018","unstructured":"Weissman GE, Hubbard RA, Ungar LH, Harhay MO, Greene CS, Himes BE, Halpern SD (2018) Inclusion of unstructured clinical text improves early prediction of death or prolonged ICU stay*. Crit Care Med 46:1125\u20131132. \nhttps:\/\/doi.org\/10.1097\/CCM.0000000000003148","journal-title":"Crit Care Med"},{"key":"2174_CR12","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.jss.2017.09.002","volume":"221","author":"JN Cooper","year":"2018","unstructured":"Cooper JN, Minneci PC, Deans KJ (2018) Postoperative neonatal mortality prediction using superlearning. J Surg Res 221:311\u2013319. \nhttps:\/\/doi.org\/10.1016\/j.jss.2017.09.002","journal-title":"J Surg Res"},{"key":"2174_CR13","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.ijmedinf.2017.10.002","volume":"108","author":"A Awad","year":"2017","unstructured":"Awad A, Bader-El-Den M, McNicholas J, Briggs J (2017) Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach. Int J Med Inform 108:185\u2013195. \nhttps:\/\/doi.org\/10.1016\/j.ijmedinf.2017.10.002","journal-title":"Int J Med Inform"},{"key":"2174_CR14","unstructured":"Senzhang Wang, Zhoujun Li, Wenhan Chao, Qinghua Cao (2012) Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning. In: The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, Brisbane, pp 1\u20138"},{"key":"2174_CR15","unstructured":"Li D, Djulovic A, Xu J (2013) A study of kNN using ICU multivariate time series data. Proc Int Conf Data Min Eds R Stahlbock GM Weiss 211\u2013217"},{"key":"2174_CR16","doi-asserted-by":"publisher","unstructured":"Rajkomar A, Oren E, Chen K et al (2018) Scalable and accurate deep learning with electronic health records. Npj Digit Med 1. \nhttps:\/\/doi.org\/10.1038\/s41746-018-0029-1","DOI":"10.1038\/s41746-018-0029-1"},{"key":"2174_CR17","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1109\/TKDE.2005.67","volume":"17","author":"A Bernstein","year":"2005","unstructured":"Bernstein A, Provost F, Hill S (2005) Toward intelligent assistance for a data mining process: an ontology-based approach for cost-sensitive classification. IEEE Trans Knowl Data Eng 17:503\u2013518. \nhttps:\/\/doi.org\/10.1109\/TKDE.2005.67","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2174_CR18","doi-asserted-by":"publisher","first-page":"160035","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"Johnson AEW, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (2016) MIMIC-III, a freely accessible critical care database. Sci Data 3:160035. \nhttps:\/\/doi.org\/10.1038\/sdata.2016.35","journal-title":"Sci Data"},{"key":"2174_CR19","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1177\/0272989X9601600409","volume":"16","author":"W Penny","year":"1996","unstructured":"Penny W, Frost D (1996) Neural networks in clinical medicine. Med Decis Mak 16:386\u2013398. \nhttps:\/\/doi.org\/10.1177\/0272989X9601600409","journal-title":"Med Decis Mak"},{"key":"2174_CR20","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.jhydrol.2010.05.040","volume":"389","author":"CL Wu","year":"2010","unstructured":"Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146\u2013167. \nhttps:\/\/doi.org\/10.1016\/j.jhydrol.2010.05.040","journal-title":"J Hydrol"},{"key":"2174_CR21","unstructured":"Allen D (1993) Automatic one-hot re-encoding for FPLs. In: Selected Papers from the Second International Workshop on Field-Programmable Logic and Applications, Field-Programmable Gate Arrays: Architectures and Tools for Rapid Prototyping. Springer-Verlag, London, pp 71\u201377"},{"key":"2174_CR22","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.1016\/S0140-6736(10)60278-4","volume":"375","author":"F Cools","year":"2010","unstructured":"Cools F, Askie LM, Offringa M, Asselin JM, Calvert SA, Courtney SE, Dani C, Durand DJ, Gerstmann DR, Henderson-Smart DJ, Marlow N, Peacock JL, Pillow JJ, Soll RF, Thome UH, Truffert P, Schreiber MD, van Reempts P, Vendettuoli V, Vento G, PreVILIG collaboration (2010) Elective high-frequency oscillatory versus conventional ventilation in preterm infants: a systematic review and meta-analysis of individual patients\u2019 data. Lancet 375:2082\u20132091. \nhttps:\/\/doi.org\/10.1016\/S0140-6736(10)60278-4","journal-title":"Lancet"},{"key":"2174_CR23","doi-asserted-by":"publisher","first-page":"441","DOI":"10.2471\/BLT.06.034421","volume":"85","author":"Z Mei","year":"2007","unstructured":"Mei Z (2007) Standard deviation of anthropometric Z-scores as a data quality assessment tool using the 2006 WHO growth standards: a cross country analysis. Bull World Health Organ 85:441\u2013448. \nhttps:\/\/doi.org\/10.2471\/BLT.06.034421","journal-title":"Bull World Health Organ"},{"key":"2174_CR24","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/BRACIS.2013.10","volume-title":"2013 Brazilian Conference on Intelligent Systems","author":"N Spolaor","year":"2013","unstructured":"Spolaor N, Cherman EA, Monard MC, Lee HD (2013) ReliefF for multi-label feature selection. In: 2013 Brazilian Conference on Intelligent Systems. IEEE, Fortaleza, pp 6\u201311"},{"key":"2174_CR25","unstructured":"Gu Q, Li Z, Han J Generalized Fisher score for feature selection. 8"},{"key":"2174_CR26","unstructured":"Park H, Kwon S, Kwon H-C Complete Gini-index text (GIT) feature-selection algorithm for text classification. 7"},{"key":"2174_CR27","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/TBME.2013.2290800","volume":"61","author":"F Alonso-Atienza","year":"2014","unstructured":"Alonso-Atienza F, Morgado E, Fernandez-Martinez L et al (2014) Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans Biomed Eng 61:832\u2013840. \nhttps:\/\/doi.org\/10.1109\/TBME.2013.2290800","journal-title":"IEEE Trans Biomed Eng"},{"key":"2174_CR28","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1057\/jors.2008.179","volume":"60","author":"H-W Cho","year":"2009","unstructured":"Cho H-W, Baek SH, Youn E, Jeong MK, Taylor A (2009) A two-stage classification procedure for near-infrared spectra based on multi-scale vertical energy wavelet thresholding and SVM-based gradient-recursive feature elimination. J Oper Res Soc 60:1107\u20131115. \nhttps:\/\/doi.org\/10.1057\/jors.2008.179","journal-title":"J Oper Res Soc"},{"key":"2174_CR29","unstructured":"Ke G, Meng Q, Finley T, et al (2017) LightGBM: a highly efficient gradient boosting decision tree. In: Guyon I, Luxburg UV, Bengio S, et al (eds) Advances in Neural Information Processing Systems 30. Curran Associates, Inc., pp 3146\u20133154"},{"key":"2174_CR30","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1145\/2939672.2939785","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD \u201916","author":"T Chen","year":"2016","unstructured":"Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD \u201916. ACM Press, San Francisco, pp 785\u2013794"},{"key":"2174_CR31","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random Forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43:1947\u20131958. \nhttps:\/\/doi.org\/10.1021\/ci034160g","journal-title":"J Chem Inf Comput Sci"},{"key":"2174_CR32","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/S0300-9572(98)00012-4","volume":"36","author":"PM Dodek","year":"1998","unstructured":"Dodek PM, Wiggs BR (1998) Logistic regression model to predict outcome after in-hospital cardiac arrest: validation, accuracy, sensitivity and specificity. Resuscitation 36:201\u2013208. \nhttps:\/\/doi.org\/10.1016\/S0300-9572(98)00012-4","journal-title":"Resuscitation"},{"key":"2174_CR33","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.resuscitation.2010.09.480","volume":"82","author":"A Moon","year":"2011","unstructured":"Moon A, Cosgrove JF, Lea D, Fairs A, Cressey DM (2011) An eight year audit before and after the introduction of modified early warning score (MEWS) charts, of patients admitted to a tertiary referral intensive care unit after CPR. Resuscitation 82:150\u2013154. \nhttps:\/\/doi.org\/10.1016\/j.resuscitation.2010.09.480","journal-title":"Resuscitation"},{"key":"2174_CR34","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1001\/archdermatol.2011.267","volume":"147","author":"C Carney","year":"2011","unstructured":"Carney C (2011) A new classification system for grading the severity of onychomycosis: onychomycosis severity index. Arch Dermatol 147:1277\u20131282. \nhttps:\/\/doi.org\/10.1001\/archdermatol.2011.267","journal-title":"Arch Dermatol"},{"key":"2174_CR35","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/s40484-016-0081-2","volume":"4","author":"Y Jiao","year":"2016","unstructured":"Jiao Y, Du P (2016) Performance measures in evaluating machine learning based bioinformatics predictors for classifications. Quant Biol 4:320\u2013330. \nhttps:\/\/doi.org\/10.1007\/s40484-016-0081-2","journal-title":"Quant Biol"},{"key":"2174_CR36","doi-asserted-by":"publisher","first-page":"e0159654","DOI":"10.1371\/journal.pone.0159654","volume":"11","author":"C Figuera","year":"2016","unstructured":"Figuera C, Irusta U, Morgado E, Aramendi E, Ayala U, Wik L, Kramer-Johansen J, Eftest\u00f8l T, Alonso-Atienza F (2016) Machine learning techniques for the detection of shockable rhythms in automated external defibrillators. PLoS One 11:e0159654. \nhttps:\/\/doi.org\/10.1371\/journal.pone.0159654","journal-title":"PLoS One"},{"key":"2174_CR37","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-0-387-21606-5_7","volume-title":"The elements of statistical learning","author":"T Hastie","year":"2001","unstructured":"Hastie T, Friedman J, Tibshirani R (2001) The elements of statistical learning. In: The elements of statistical learning. Springer New York, New York, pp 193\u2013224"},{"key":"2174_CR38","doi-asserted-by":"publisher","first-page":"P3","DOI":"10.1186\/cc5790","volume":"11","author":"J Alves-Filho","year":"2007","unstructured":"Alves-Filho J, Freitas A, Spiller F, Souto FO, Paula-Neto H, Silva JS, Cunha FQ (2007) Fourth international symposium on intensive care and emergency medicine for Latin America. Crit Care 11:P3. \nhttps:\/\/doi.org\/10.1186\/cc5790","journal-title":"Crit Care"},{"key":"2174_CR39","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/S1386-5056(02)00014-X","volume":"65","author":"A Junger","year":"2002","unstructured":"Junger A, B\u00f6ttger S, Engel J et al (2002) Automatic calculation of a modified APACHE II score using a patient data management system (PDMS). Int J Med Inform 65:145\u2013157. \nhttps:\/\/doi.org\/10.1016\/S1386-5056(02)00014-X","journal-title":"Int J Med Inform"},{"key":"2174_CR40","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1016\/j.patcog.2007.04.009","volume":"40","author":"Y Sun","year":"2007","unstructured":"Sun Y, Kamel MS, Wong AKC, Wang Y (2007) Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn 40:3358\u20133378. \nhttps:\/\/doi.org\/10.1016\/j.patcog.2007.04.009","journal-title":"Pattern Recogn"},{"key":"2174_CR41","doi-asserted-by":"crossref","unstructured":"Zadrozny B, Langford J, Abe N (2003) Cost-sensitive learning by cost-proportionate example weighting. In: Third IEEE International Conference on Data Mining. IEEE Comput. Soc, Melbourne, pp 435\u2013442","DOI":"10.1109\/ICDM.2003.1250950"},{"key":"2174_CR42","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/S0735-1097(02)02863-2","volume":"41","author":"R Sicari","year":"2003","unstructured":"Sicari R, Pasanisi E, Venneri L, Landi P, Cortigiani L, Picano E, Echo Persantine International Cooperative (EPIC) Study Group, Echo Dobutamine International Cooperative (EDIC) Study Group (2003) Stress echo results predict mortality: a large-scale multicenter prospective international study. J Am Coll Cardiol 41:589\u2013595. \nhttps:\/\/doi.org\/10.1016\/S0735-1097(02)02863-2","journal-title":"J Am Coll Cardiol"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02174-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11517-020-02174-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02174-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T01:03:39Z","timestamp":1600218219000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11517-020-02174-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,20]]},"references-count":42,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["2174"],"URL":"https:\/\/doi.org\/10.1007\/s11517-020-02174-0","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,20]]},"assertion":[{"value":"23 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}