Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classificat. Regress. Trees. CRC Press (1984). https://doi.org/10.1201/9781315139470

Article  Google Scholar 

Brier, G.W.: Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 78, 1–3 (1950). https://doi.org/10.1175/1520-0493(1950)078%3c0001:VOFEIT%3e2.0.CO;2

Article  Google Scholar 

Buntin, M.B., Zaslavsky, A.M.: Too much ado about two-part models and transformation? J. Health Econ. 23, 525–542 (2004). https://doi.org/10.1016/j.jhealeco.2003.10.005

Article  PubMed  Google Scholar 

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. 785–794 (2016). https://doi.org/10.1145/2939672.2939785

Chen, T., Liu, C., Huang, M., Cheng, X., Zhou, L.: Adverse drug reaction prediction and feature importance mining based on SIDER dataset. In: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022). pp. 74–81. SPIE (2023). https://doi.org/10.1117/12.2675459

Chye, J., Lim, C.: Very low birth weight infants mortality and predictive risk factors. Singapore Med. J. 40, 565–570 (1999). https://doi.org/10.4103/ijpvm.IJPVM_300_20

Article  CAS  PubMed  Google Scholar 

DeSalvo, K.B., Bloser, N., Reynolds, K., He, J., Muntner, P.: Mortality prediction with a single general self-rated health question. J. Gen. Intern. Med. 21, 267–275 (2006). https://doi.org/10.1111/j.1525-1497.2005.00291.x

Article  PubMed  PubMed Central  Google Scholar 

Ebeling, M., Rau, R., Malmström, H., Ahlbom, A., Modig, K.: The rate by which mortality increase with age is the same for those who experienced chronic disease as for the general population. Age Ageing 50, 1633–1640 (2021). https://doi.org/10.1093/ageing/afab085

Article  PubMed  PubMed Central  Google Scholar 

Einav, L., Finkelstein, A., Mullainathan, S., Obermeyer, Z.: Predictive modeling of US health care spending in late life. Science 360(6396), 1462–1465 (2018). https://doi.org/10.1126/science.aar5045

Article  CAS  PubMed  PubMed Central  Google Scholar 

Elhazmi, A., Al-Omari, A., Sallam, H., Mufti, H.N., Rabie, A.A., Alshahrani, M., Mady, A., Alghamdi, A., Altalaq, A., Azzam, M.H., Sindi, A., Kharaba, A., Al-Aseri, Z.A., Almekhlafi, G.A., Tashkandi, W., Alajmi, S.A., Faqihi, F., Alharthy, A., Al-Tawfiq, J.A., Melibari, R.G., Al-Hazzani, W., Arabi, Y.M.: Machine learning decision tree algorithm role for predicting mortality in critically ill adult COVID-19 patients admitted to the ICU. J. Infect. Public Health 15, 826–834 (2022). https://doi.org/10.1016/j.jiph.2022.06.008

Article  PubMed  PubMed Central  Google Scholar 

Flach, P., Hernandez-Orallo, J., Ferri, C.: A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance. In: Proceedings of the 28th International Conference on Machine Learning, ICML (2011).

Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102, 359–378 (2007). https://doi.org/10.1198/016214506000001437

Article  CAS  Google Scholar 

Hailpern, S.M., Visintainer, P.F.: Odds ratios and logistic regression: further examples of their use and interpretation. Stand. Genomic Sci. 3, 213–225 (2003). https://doi.org/10.1177/1536867X0300300301

Article  Google Scholar 

Han, P.K.J., Lee, M., Reeve, B.B., Mariotto, A.B., Wang, Z., Hays, R.D., Yabroff, K.R., Topor, M., Feuer, E.J.: Development of a prognostic model for six-month mortality in older adults with declining health. J. Pain Symptom Manag. 43, 527–539 (2012). https://doi.org/10.1016/j.jpainsymman.2011.04.015

Article  Google Scholar 

Hand, D.J.: Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach. Learn. 77, 103–123 (2009). https://doi.org/10.1007/s10994-009-5119-5

Article  Google Scholar 

Hou, N., Li, M., He, L., Xie, B., Wang, L., Zhang, R., Yu, Y., Sun, X., Pan, Z., Wang, K.: Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J. Transl. Med. 18, 462 (2020). https://doi.org/10.1186/s12967-020-02620-5

Article  CAS  PubMed  PubMed Central  Google Scholar 

Huang, L.-Y., Chen, F.-Y., Jhou, M.-J., Kuo, C.-H., Wu, C.-Z., Lu, C.-H., Chen, Y.-L., Pei, D., Cheng, Y.-F., Lu, C.-J.: Comparing multiple linear regression and machine learning in predicting diabetic urine albumin–creatinine ratio in a 4-year follow-up study. J. Clin. Med. 11, 3661 (2022). https://doi.org/10.3390/jcm11133661

Article  CAS  PubMed  PubMed Central  Google Scholar 

Katsaragakis, S., Papadimitropoulos, K., Antonakis, P., Strergiopoulos, S., Konstadoulakis, M.M., Androulakis, G.: Comparison of acute physiology and chronic health evaluation II (APACHE II) and simplified acute physiology score II (SAPS II) scoring systems in a single Greek intensive care unit. Crit. Care Med. 28, 426–432 (2000). https://doi.org/10.1097/00003246-200002000-00023

Article  CAS  PubMed  Google Scholar 

Kern, D.M., Wasser, T.: Analysis of health care costs containing a large proportion of $0 data using traditional and zero-inflated gamma regression models. Value Health 16, A21 (2013). https://doi.org/10.1016/j.jval.2013.03.124

Article  Google Scholar 

Levy, J.J., O’Malley, A.J.: Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning. BMC Med. Res. Methodol. 20(1), 171 (2020). https://doi.org/10.1186/s12874-020-01046-3

Article  PubMed  PubMed Central  Google Scholar 

Lin, J., Qi, C., Wan, H., Min, J., Chen, J., Zhang, K., Zhang, L.: Prediction of cross-tension strength of self-piercing riveted joints using finite element simulation and XGBoost algorithm. Chin. J. Mech. Eng. 34, 36 (2021). https://doi.org/10.1186/s10033-021-00551-w

Article  CAS  Google Scholar 

Maley, J.H., Wanis, K.N., Young, J.G., Celi, L.A.: Mortality prediction models, causal effects, and end-of-life decision making in the intensive care unit. BMJ Health Care Inform (2020). https://doi.org/10.1136/bmjhci-2020-100220

Article  PubMed  PubMed Central  Google Scholar 

Manning, W.G.: A two-part model of the demand for medical care : preliminary results from the Health Insurance Study, (1981)

Mihaylova, B., Briggs, A., O’Hagan, A., Thompson, S.G.: Review of statistical methods for analysing healthcare resources and costs. Health Econ. 20, 897–916 (2011). https://doi.org/10.1002/hec.1653

Article  PubMed  Google Scholar 

Palmer, S., West, P., Patrick, D., Glynn, M.: Mortality indices in resource allocation. J. Public Health 1, 275–281 (1979). https://doi.org/10.1093/oxfordjournals.pubmed.a043252

Article  CAS  Google Scholar 

Patel, P.A., Grant, B.J.B.: Application of mortality prediction systems to individual intensive care units. Intensive Care Med. 25, 977–982 (1999). https://doi.org/10.1007/s001340050992

Article  CAS  PubMed  Google Scholar 

Pesantez-Narvaez, J., Guillen, M., Alcañiz, M.: Predicting motor insurance claims using telematics data—XGBoost versus logistic regression. Risks. 7, 70 (2019). https://doi.org/10.3390/risks7020070

Article  Google Scholar 

Christopher A. Powers, Christina M. Meyer, M. Christopher Roebuck, Baze Vaziri: Predictive modeling of total healthcare costs using pharmacy claims data: a comparison of alternative econometric cost modeling techniques. Medical Care, 43, 1065–1072 (2005). https://doi.org/10.1097/01.mlr.0000182408.54390.00

Puterman, E., Weiss, J., Hives, B.A., Gemmill, A., Karasek, D., Mendes, W.B., Rehkopf, D.H.: Predicting mortality from 57 economic, behavioral, social, and psychological factors. Proc. Natl. Acad. Sci. u.s.a. 117, 16273–16282 (2020). https://doi.org/10.1073/pnas.1918455117

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schneider, C., Aubert, C.E., Del Giovane, C., Donzé, J.D., Gastens, V., Bauer, D.C., Blum, M.R., Dalleur, O., Henrard, S., Knol, W., O’Mahony, D., Curtin, D., Lee, S.J., Aujesky, D., Rodondi, N., Feller, M.: Comparison of 6 mortality risk scores for prediction of 1-year mortality risk in older adults with multimorbidity. JAMA Netw. Open 5, e2223911 (2022). https://doi.org/10.1001/jamanetworkopen.2022.23911

Article  PubMed  PubMed Central  Google Scholar 

Tang, C.Q., Li, J.Q., Xu, D.Y., Liu, X.B., Hou, W.J., Lyu, K.Y., Xiao, S.C., Xia, Z.F.: Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients. Zhonghua Shao Shang Za Zhi 34, 343–348 (2018). https://doi.org/10.3760/cma.j.issn.1009-2587.2018.06.006

Article  CAS  PubMed  Google Scholar 

Wilson, R.H.L., Newman, E.J., Newman, H.W.: Diurnal variation in rate of alcohol metabolism. J. Appl. Physiol. 8, 556–558 (1956). https://doi.org/10.1152/jappl.1956.8.5.556

Article  CAS  PubMed  Google Scholar 

Yeatts, J.P., Sangvai, D.: HCC coding, risk adjustment, and physician income: what you need to know. Fam. Pract. Manag. 23, 24–27 (2016)

PubMed  Google Scholar 

Zhang, G., Xu, J., Yu, M., Yuan, J., Chen, F.: A machine learning approach for mortality prediction only using non-invasive parameters. Med. Biol. Eng. Comput. 58, 2195–2238 (2020). https://doi.org/10.1007/s11517-020-02174-0