{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:24:14Z","timestamp":1740122654388,"version":"3.37.3"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T00:00:00Z","timestamp":1681430400000},"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":["Appl Intell"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s10489-023-04577-6","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T04:02:24Z","timestamp":1681444944000},"page":"20469-20484","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reverse survival model (RSM): a pipeline for explaining predictions of deep survival models"],"prefix":"10.1007","volume":"53","author":[{"given":"Mohammad R.","family":"Rezaei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza Saadati","family":"Fard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ebrahim","family":"Pourjafari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Navid","family":"Ziaei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Sameizadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Shafiee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Alavinia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mansour","family":"Abolghasemian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9881-7251","authenticated-orcid":false,"given":"Nick","family":"Sajadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,14]]},"reference":[{"key":"4577_CR1","doi-asserted-by":"crossref","unstructured":"Nagpal C, Li XR, Dubrawski A (2021) Deep survival machines: fully parametric survival regression and representation learning for censored data with competing risks. IEEE J Biomed Health Inf","DOI":"10.1109\/JBHI.2021.3052441"},{"key":"4577_CR2","doi-asserted-by":"crossref","unstructured":"Lee C, Zame WR, Yoon J, van der Schaar M (2018) Deephit: a deep learning approach to survival analysis with competing risks. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11842"},{"issue":"1","key":"4577_CR3","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1109\/TBME.2019.2909027","volume":"67","author":"C Lee","year":"2019","unstructured":"Lee C, Yoon J, Van Der Schaar M (2019) Dynamic-deephit: a deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Trans Biomed Eng 67(1):122\u2013133","journal-title":"IEEE Trans Biomed Eng"},{"key":"4577_CR4","unstructured":"Miscouridou X, Perotte A, Elhadad N, Ranganath R (2018) Deep survival analysis: nonparametrics and missingness. In: Machine learning for healthcare conference. PMLR, pp 244\u2013256"},{"key":"4577_CR5","doi-asserted-by":"crossref","unstructured":"Therneau TM, Grambsch PM (2000) The cox model. In: Modeling survival data: extending the cox model. Springer, pp 39\u201377","DOI":"10.1007\/978-1-4757-3294-8_3"},{"issue":"402","key":"4577_CR6","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1080\/01621459.1988.10478612","volume":"83","author":"B Efron","year":"1988","unstructured":"Efron B (1988) Logistic regression, survival analysis, and the kaplan-meier curve. J American Stat Association 83(402):414\u2013425","journal-title":"J American Stat Association"},{"issue":"13-14","key":"4577_CR7","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1016\/j.spl.2010.02.020","volume":"80","author":"H Ishwaran","year":"2010","unstructured":"Ishwaran H, Kogalur UB (2010) Consistency of random survival forests. Stat Probability Lett 80(13-14):1056\u20131064","journal-title":"Stat Probability Lett"},{"issue":"1","key":"4577_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12874-018-0482-1","volume":"18","author":"JL Katzman","year":"2018","unstructured":"Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y (2018) Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med Res Methodol 18(1):1\u201312","journal-title":"BMC Med Res Methodol"},{"key":"4577_CR9","unstructured":"Thiagarajan JJ, Sattigeri P, Rajan D, Venkatesh B (2020) Calibrating healthcare ai: towards reliable and interpretable deep predictive models. arXiv:2004.14480"},{"key":"4577_CR10","doi-asserted-by":"crossref","unstructured":"Ozen E, Orailoglu A (2019) Sanity-check: boosting the reliability of safety-critical deep neural network applications. In: 2019 IEEE 28th asian test symposium (ATS). IEEE, pp 7\u201375","DOI":"10.1109\/ATS47505.2019.000-8"},{"key":"4577_CR11","doi-asserted-by":"crossref","unstructured":"Hanif MA, Khalid F, Putra RVW, Rehman S, Shafique M (2018) Robust machine learning systems: reliability and security for deep neural networks. In: 2018 IEEE 24th international symposium on on-line testing and robust system design (IOLTS). IEEE, pp 257\u2013260","DOI":"10.1109\/IOLTS.2018.8474192"},{"key":"4577_CR12","doi-asserted-by":"crossref","unstructured":"Chung I, Kim S, Lee J, Kim KJ, Hwang SJ, Yang E (2020) Deep mixed effect model using gaussian processes: a personalized and reliable prediction for healthcare. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 3649\u20133657","DOI":"10.1609\/aaai.v34i04.5773"},{"key":"4577_CR13","doi-asserted-by":"crossref","unstructured":"Rezaei MR, Popovic MR, Lankarany M, Yousefi A (2022) Deep discriminative direct decoders for high-dimensional time-series analysis. arXiv:2205.10947","DOI":"10.51628\/001c.85131"},{"key":"4577_CR14","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) why should i trust you? explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"4577_CR15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-11817-6","volume":"7","author":"S Yousefi","year":"2017","unstructured":"Yousefi S, Amrollahi F, Amgad M, Dong C, Lewis JE, Song C, Gutman DA, Halani SH, Velazquez Vega JE, Brat DJ et al (2017) Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci Rep 7(1):1\u201311","journal-title":"Sci Rep"},{"issue":"3","key":"4577_CR16","doi-asserted-by":"crossref","first-page":"191","DOI":"10.2217\/cer.15.12","volume":"4","author":"B Gallego","year":"2015","unstructured":"Gallego B, Walter SR, Day RO, Dunn AG, Sivaraman V, Shah N, Longhurst CA, Coiera E (2015) Bringing cohort studies to the bedside: framework for a \u2019green button\u2019to support clinical decision-making. J Comparative Effect Res 4(3):191\u2013197","journal-title":"J Comparative Effect Res"},{"issue":"1","key":"4577_CR17","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/2408736.2408740","volume":"14","author":"J Sun","year":"2012","unstructured":"Sun J, Wang F, Hu J, Edabollahi S (2012) Supervised patient similarity measure of heterogeneous patient records. Acm Sigkdd Explor Newsletter 14(1):16\u201324","journal-title":"Acm Sigkdd Explor Newsletter"},{"issue":"1","key":"4577_CR18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-7439(99)00047-7","volume":"50","author":"R De Maesschalck","year":"2000","unstructured":"De Maesschalck R, Jouan-Rimbaud D, Massart DL (2000) The mahalanobis distance. Chemometrics Intell Lab Syst 50(1):1\u201318","journal-title":"Chemometrics Intell Lab Syst"},{"issue":"5","key":"4577_CR19","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1089\/cmb.2015.0189","volume":"23","author":"Y Li","year":"2016","unstructured":"Li Y, Chen C-Y, Wasserman WW (2016) Deep feature selection: theory and application to identify enhancers and promoters. J Comput Biol 23(5):322\u2013336","journal-title":"J Comput Biol"},{"key":"4577_CR20","unstructured":"Che Z, Purushotham S, Khemani R, Liu Y (2016) Interpretable deep models for icu outcome prediction. In: AMIA annual symposium proceedings. American Medical Informatics Association, vol 2016, pp 371"},{"key":"4577_CR21","doi-asserted-by":"crossref","unstructured":"Fuglede B, Topsoe F (2004) Jensen-shannon divergence and hilbert space embedding. In: International symposium onInformation theory, 2004. ISIT 2004. Proceedings. IEEE, p 31","DOI":"10.1109\/ISIT.2004.1365067"},{"key":"4577_CR22","doi-asserted-by":"crossref","unstructured":"Connor R, Cardillo FA, Moss R, Rabitti F (2013) Evaluation of jensen-shannon distance over sparse data. In: International conference on similarity search and applications. Springer, p 163\u2013168","DOI":"10.1007\/978-3-642-41062-8_16"},{"key":"4577_CR23","doi-asserted-by":"crossref","unstructured":"Massey Jr FJ (1951) The kolmogorov-smirnov test for goodness of fit. J American Stat Association 46(253):68\u201378","DOI":"10.1080\/01621459.1951.10500769"},{"key":"4577_CR24","unstructured":"Johnson A, Bulgarelli L, Pollard T, Horng S, Celi L, Mark R (2020) MIMIC-IV (Version 0.4) PhysioNet"},{"issue":"4","key":"4577_CR25","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1111\/risa.12865","volume":"38","author":"SH Moolgavkar","year":"2018","unstructured":"Moolgavkar SH, Chang ET, Watson HN, Lau EC (2018) An assessment of the cox proportional hazards regression model for epidemiologic studies. Risk Anal 38(4):777\u2013794","journal-title":"Risk Anal"},{"issue":"8","key":"4577_CR26","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1038\/ki.2008.328","volume":"74","author":"FW Dekker","year":"2008","unstructured":"Dekker FW, De Mutsert R, Van Dijk PC, Zoccali C, Jager KJ (2008) Survival analysis: time-dependent effects and time-varying risk factors. Kidney Int 74(8):994\u2013997","journal-title":"Kidney Int"},{"issue":"2","key":"4577_CR27","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1176\/appi.ajp.2016.16010077","volume":"174","author":"Y Barak-Corren","year":"2017","unstructured":"Barak-Corren Y, Castro VM, Javitt S, Hoffnagle AG, Dai Y, Perlis RH, Nock MK, Smoller JW, Reis BY (2017) Predicting suicidal behavior from longitudinal electronic health records. American J Psych 174(2):154\u2013162","journal-title":"American J Psych"},{"key":"4577_CR28","doi-asserted-by":"crossref","unstructured":"Wells BJ, Chagin KM, Nowacki AS, Kattan MW (2013) Strategies for handling missing data in electronic health record derived data. Egems, vol 1(3)","DOI":"10.13063\/2327-9214.1035"},{"key":"4577_CR29","doi-asserted-by":"crossref","first-page":"107501","DOI":"10.1016\/j.patcog.2020.107501","volume":"107","author":"A Nazabal","year":"2020","unstructured":"Nazabal A, Olmos PM, Ghahramani Z, Valera I (2020) Handling incomplete heterogeneous data using vaes. Pattern Recogn 107:107501","journal-title":"Pattern Recogn"},{"key":"4577_CR30","doi-asserted-by":"crossref","unstructured":"Khan FM, Zubek VB (2008) Support vector regression for censored data (svrc): a novel tool for survival analysis. In: 2008 Eighth IEEE international conference on data mining. IEEE, pp 863\u2013868","DOI":"10.1109\/ICDM.2008.50"},{"issue":"2","key":"4577_CR31","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1038\/sj.bjc.6601118","volume":"89","author":"TG Clark","year":"2003","unstructured":"Clark TG, Bradburn MJ, Love SB, Altman DG (2003) Survival analysis part i: basic concepts and first analyses. British J Cancer 89(2):232\u2013238","journal-title":"British J Cancer"},{"issue":"5","key":"4577_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/cc2955","volume":"8","author":"V Bewick","year":"2004","unstructured":"Bewick V, Cheek L, Ball J (2004) Statistics review 12: survival analysis. Critic Care 8 (5):1\u20136","journal-title":"Critic Care"},{"issue":"8","key":"4577_CR33","doi-asserted-by":"crossref","first-page":"3163","DOI":"10.1109\/JBHI.2021.3052441","volume":"25","author":"C Nagpal","year":"2021","unstructured":"Nagpal C, Li X, Dubrawski A (2021) Deep survival machines: fully parametric survival regression and representation learning for censored data with competing risks. IEEE J Biomed Health Inf 25 (8):3163\u20133175","journal-title":"IEEE J Biomed Health Inf"},{"issue":"3","key":"4577_CR34","first-page":"841","volume":"2","author":"H Ishwaran","year":"2008","unstructured":"Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008) Random survival forests. Annal Appl Stat 2(3):841\u2013860","journal-title":"Annal Appl Stat"},{"key":"4577_CR35","unstructured":"Pourjafari E, Ziaei N, Rezaei MR, Sameizadeh A, Shafiee M, Alavinia M, Abolghasemian M, Sajadi N (2022) Survival seq2seq: A survival model based on sequence to sequence architecture. arXiv:2204.04542"},{"issue":"2","key":"4577_CR36","first-page":"1","volume":"1","author":"S-H Cha","year":"2007","unstructured":"Cha S-H (2007) Comprehensive survey on distance\/similarity measures between probability density functions. City 1(2):1","journal-title":"City"},{"key":"4577_CR37","unstructured":"Molchanov D, Ashukha A, Vetrov D (2017)"},{"key":"4577_CR38","unstructured":"Chang C-H, Rampasek L, Goldenberg A (2017) Dropout feature ranking for deep learning models. arXiv:1712.08645"},{"key":"4577_CR39","first-page":"173","volume":"109088","author":"M Naaman","year":"2021","unstructured":"Naaman M (2021) On the tight constant in the multivariate dvoretzky\u2013kiefer\u2013wolfowitz inequality. Stat Probability Lett 109088:173","journal-title":"Stat Probability Lett"},{"issue":"4","key":"4577_CR40","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","volume":"2","author":"H Abdi","year":"2010","unstructured":"Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdisciplinary Rev: Comput Stat 2(4):433\u2013459","journal-title":"Wiley Interdisciplinary Rev: Comput Stat"},{"key":"4577_CR41","doi-asserted-by":"crossref","first-page":"54776","DOI":"10.1109\/ACCESS.2020.2980942","volume":"8","author":"GT Reddy","year":"2020","unstructured":"Reddy GT, Reddy MPK, Lakshmanna K, Kaluri R, Rajput DS, Srivastava G, Baker T (2020) Analysis of dimensionality reduction techniques on big data. IEEE Acc 8:54776\u201354788","journal-title":"IEEE Acc"},{"issue":"3","key":"4577_CR42","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s521-001-8051-z","volume":"10","author":"R Rosipal","year":"2001","unstructured":"Rosipal R, Girolami M, Trejo LJ, Cichocki A (2001) Kernel pca for feature extraction and de-noising in nonlinear regression. Neural Comput Appl 10(3):231\u2013243","journal-title":"Neural Comput Appl"},{"key":"4577_CR43","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst, vol 30"},{"key":"4577_CR44","doi-asserted-by":"crossref","unstructured":"Lagakos SW (1979) General right censoring and its impact on the analysis of survival data. Biometrics, pp 139\u2013156","DOI":"10.2307\/2529941"},{"issue":"1","key":"4577_CR45","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1146\/annurev.publhealth.18.1.83","volume":"18","author":"K-M Leung","year":"1997","unstructured":"Leung K-M, Elashoff RM, Afifi AA (1997) Censoring issues in survival analysis. Annual Rev Pub Health 18(1):83\u2013104","journal-title":"Annual Rev Pub Health"},{"issue":"26","key":"4577_CR46","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.1200\/JCO.2011.38.7589","volume":"30","author":"JG Ibrahim","year":"2012","unstructured":"Ibrahim JG, Chu H, Chen M-H (2012) Missing data in clinical studies: issues and methods. J Clinic Oncology 30(26):3297","journal-title":"J Clinic Oncology"},{"issue":"9","key":"4577_CR47","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1016\/j.pmrj.2015.07.011","volume":"7","author":"KL Sainani","year":"2015","unstructured":"Sainani KL (2015) Dealing with missing data. PM&R 7(9):990\u2013994","journal-title":"PM&R"},{"issue":"1","key":"4577_CR48","first-page":"1","volume":"8","author":"Z Che","year":"2018","unstructured":"Che Z, Purushotham S, Cho K, Sontag D, Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8(1):1\u201312","journal-title":"Sci Rep"},{"issue":"1","key":"4577_CR49","first-page":"2529","volume":"13","author":"J Kim","year":"2012","unstructured":"Kim J, Scott CD (2012) Robust kernel density estimation. J Mach Learn Res 13(1):2529\u20132565","journal-title":"J Mach Learn Res"},{"issue":"24","key":"4577_CR50","doi-asserted-by":"crossref","first-page":"3927","DOI":"10.1002\/sim.2427","volume":"24","author":"L Antolini","year":"2005","unstructured":"Antolini L, Boracchi P, Biganzoli E (2005) A time-dependent discrimination index for survival data. Stat Med 24(24):3927\u20133944","journal-title":"Stat Med"},{"issue":"3","key":"4577_CR51","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai T, Draxler RR (2014) Root mean square error (rmse) or mean absolute error (mae)?\u2013arguments against avoiding rmse in the literature. Geosci Model Dev 7(3):1247\u20131250","journal-title":"Geosci Model Dev"},{"issue":"1","key":"4577_CR52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2466-15-1","volume":"15","author":"ML Vold","year":"2015","unstructured":"Vold ML, Aaseb\u00f8 U, Wilsgaard T, Melbye H (2015) Low oxygen saturation and mortality in an adult cohort: the troms\u00f8, study. BMC Pulmonary Med 15(1):1\u201312","journal-title":"BMC Pulmonary Med"},{"issue":"5","key":"4577_CR53","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1097\/CCM.0000000000004268","volume":"48","author":"SK Sahetya","year":"2020","unstructured":"Sahetya SK, Wu TD, Morgan B, Herrera P, Roldan R, Paz E, Jaymez AA, Chirinos E, Portugal J, Quispe R et al (2020) Mean airway pressure as a predictor of 90-day mortality in mechanically ventilated patients. Critic Care Med 48(5):688","journal-title":"Critic Care Med"},{"key":"4577_CR54","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.hrtlng.2022.04.004","volume":"55","author":"H Zhang","year":"2022","unstructured":"Zhang H, Tian W, Sun Y (2022) The value of anion gap for predicting the short-term all-cause mortality of critically ill patients with cardiac diseases, based on mimic-iii database. Heart Lung 55:59\u201367","journal-title":"Heart Lung"},{"key":"4577_CR55","doi-asserted-by":"crossref","unstructured":"Lacson RC, Baker B, Suresh H, Andriole K, Szolovits P, Lacson Jr E (2019) Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients. Clinical Kidney J 12(2):206\u2013212","DOI":"10.1093\/ckj\/sfy049"},{"issue":"1","key":"4577_CR56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13054-019-2683-3","volume":"24","author":"MW Kang","year":"2020","unstructured":"Kang MW, Kim J, Kim DK, Oh K-H, Joo KW, Kim YS, Han SS (2020) Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy. Crit Care 24(1):1\u20139","journal-title":"Crit Care"},{"key":"4577_CR57","doi-asserted-by":"crossref","unstructured":"Chen Z, He J, Chen C, Lu Q (2021) Association of total bilirubin with all-cause and cardiovascular mortality in the general population. Front Cardiovascular Med:615","DOI":"10.3389\/fcvm.2021.670768"},{"issue":"5","key":"4577_CR58","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.amjhyper.2005.10.023","volume":"19","author":"J Greenberg","year":"2006","unstructured":"Greenberg J (2006) Are blood pressure predictors of cardiovascular disease mortality different for prehypertensives than for hypertensives? American J Hyper 19(5):454\u2013461","journal-title":"American J Hyper"},{"issue":"5","key":"4577_CR59","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1111\/j.1365-2796.1993.tb00783.x","volume":"234","author":"G Erikssen","year":"1993","unstructured":"Erikssen G, Thaulow E, Sandvik L, Stormorken H, Erikssen J (1993) Haematocrit: a predictor of cardiovascular mortality? J Internal Med 234(5):493\u2013499","journal-title":"J Internal Med"},{"issue":"4","key":"4577_CR60","first-page":"421","volume":"34","author":"X Chen","year":"2022","unstructured":"Chen X, Lei G, Zhang X, Zhu S, Tong L (2022) Development and validation of a predictive model for the risk of 30-day death in emergency department patients. Zhonghua wei Zhong Bing ji jiu yi xue 34(4):421\u2013425","journal-title":"Zhonghua wei Zhong Bing ji jiu yi xue"},{"issue":"2","key":"4577_CR61","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1097\/CCM.0b013e3181ffe22a","volume":"39","author":"K Beier","year":"2011","unstructured":"Beier K, Eppanapally S, Bazick HS, Chang D, Mahadevappa K, Gibbons FK, Christopher KB (2011) Elevation of bun is predictive of long-term mortality in critically ill patients independent of\u2019normal\u2019creatinine. Critical Care Med 39(2):305","journal-title":"Critical Care Med"},{"issue":"2","key":"4577_CR62","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1681\/ASN.2004070602","volume":"16","author":"B Kestenbaum","year":"2005","unstructured":"Kestenbaum B, Sampson JN, Rudser KD, Patterson DJ, Seliger SL, Young B, Sherrard DJ, Andress DL (2005) Serum phosphate levels and mortality risk among people with chronic kidney disease. J Am Soc Nephrol 16(2):520\u2013528","journal-title":"J Am Soc Nephrol"},{"issue":"5","key":"4577_CR63","doi-asserted-by":"crossref","first-page":"930","DOI":"10.3324\/haematol.2013.101949","volume":"99","author":"P Msaouel","year":"2014","unstructured":"Msaouel P, Lam AP, Gundabolu K, Chrysofakis G, Yu Y, Mantzaris I, Friedman E, Verma A (2014) Abnormal platelet count is an independent predictor of mortality in the elderly and is influenced by ethnicity. Haematologica 99(5):930","journal-title":"Haematologica"},{"issue":"5","key":"4577_CR64","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1159\/000319861","volume":"32","author":"JE Miller","year":"2010","unstructured":"Miller JE, Kovesdy CP, Norris KC, Mehrotra R, Nissenson AR, Kopple JD, Kalantar-Zadeh K (2010) Association of cumulatively low or high serum calcium levels with mortality in long-term hemodialysis patients. American J Nephrology 32(5):403\u2013 413","journal-title":"American J Nephrology"},{"issue":"22","key":"4577_CR65","doi-asserted-by":"crossref","first-page":"2706","DOI":"10.1001\/archinte.159.22.2706","volume":"159","author":"NM Reddy","year":"1999","unstructured":"Reddy NM, Hall SW, MacKintosh FR (1999) Partial thromboplastin time: prediction of adverse events and poor prognosis by low abnormal values. Arch Internal Med 159(22):2706\u2013 2710","journal-title":"Arch Internal Med"},{"issue":"12","key":"4577_CR66","first-page":"1357","volume":"97","author":"R Hamed","year":"2019","unstructured":"Hamed R, Mekki I, Aouni H, Hedhli H, Zoubli A, Maaref A, Chermiti I, Bouhaja B (2019) Base excess usefulness for prediction of immediate mortality in severe trauma patients admitted to the emergency department. Tunis Med 97(12):1357\u20131361","journal-title":"Tunis Med"},{"issue":"6","key":"4577_CR67","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1016\/S0196-0644(95)70204-0","volume":"25","author":"MA Wayne","year":"1995","unstructured":"Wayne MA, Levine RL, Miller CC (1995) Use of end-tidal carbon dioxide to predict outcome in prehospital cardiac arrest. Annal Emerg Med 25(6):762\u2013767","journal-title":"Annal Emerg Med"},{"issue":"4","key":"4577_CR68","doi-asserted-by":"crossref","first-page":"0010356","DOI":"10.1371\/journal.pntd.0010356","volume":"16","author":"AM Ferreira","year":"2022","unstructured":"Ferreira AM, Santos LI, Sabino EC, Ribeiro ALP, Oliveira-da Silva L.C.d, Damasceno RF, D\u2019Angelo MFSV, Nunes MdCP, Haikal DSA (2022) Two-year death prediction models among patients with chagas disease using machine learning-based methods. PLoS Neglect Trop Diseases 16(4):0010356","journal-title":"PLoS Neglect Trop Diseases"},{"key":"4577_CR69","doi-asserted-by":"crossref","unstructured":"Vaa BE, Asrani SK, Dunn W, Kamath PS, Shah VH (2011) Influence of serum sodium on meld-based survival prediction in alcoholic hepatitis. In: Mayo clinic proceedings. Elsevier, vol 86, pp 37\u201342","DOI":"10.4065\/mcp.2010.0281"},{"key":"4577_CR70","unstructured":"Dilokthanakul N, Mediano PA, Garnelo M, Lee MC, Salimbeni H, Arulkumaran K, Shanahan M (2016) Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv:1611.02648"},{"key":"4577_CR71","doi-asserted-by":"crossref","unstructured":"Rezaei MR, Gillespie AK, Guidera JA, Nazari B, Sadri S, Frank LM, Yousefi A (2018) A comparison study of point-process filter and deep learning performance in estimating rat position using an ensemble of place cells. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 4732\u20134735","DOI":"10.1109\/EMBC.2018.8513154"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04577-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04577-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04577-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:41:56Z","timestamp":1694778116000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04577-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,14]]},"references-count":71,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["4577"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04577-6","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2023,4,14]]},"assertion":[{"value":"15 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}