Abstract
Predicting ICU mortality and finding key risk factors make sense for both doctors and patients. Although there has been a number of research pertaining to ICU mortality prediction systems and algorithms, plenty of room still exists for improvement in practical prediction results and identification of important risk factors. In this study, we use C5 decision tree model to predict mortality of ICU patients and identify key risk factors. Totally 4367 records of ICU patients from a local grade-A tertiary hospital were selected for motality prediction, including 244 dead records with demographic information and physiological parameters. In order to solve the problem of inconsistent data sampling frequency, we extracted 96 statistical indicators based on the original records, such as the kurtosis value of red blood cells (HXB_kurt), the skewness coefficient of red blood cells (HXB_skew). Totally 41 indicators as the final input of the prediction model were extracted through feature extraction method. The experimental results show that C5 decision tree model outperform C&RT, CHDID, KNN, Logistic, SVM and Random Forest in five different performance indicators. Moreover, worst-case status and state of changes in respiratory, body temperature, care level, diastolic blood pressure and age were found to be the key risk factors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chan, C.W., Farias, V.F., Escobar, G.J.: The impact of delays on service times in the intensive care unit. Manag. Sci. 63(7), 2049–2072 (2016)
Jing, L., Ming, D., Zhao, W.: Admissions optimisation and premature discharge decisions in intensive care units. Int. J. Prod. Res. 53(24), 7329–7342 (2015)
Rouzbahman, M., Jovicic, A., Chignell, M.: Can cluster-boosted regression improve prediction of death and length of stay in the ICU? IEEE J. Biomed. Health Inform. 21(3), 851–858 (2016)
Ghosh, Shameek, Feng, M., Nguyen, H., Li, J.: Hypotension risk prediction via sequential contrast patterns of ICU blood pressure. IEEE J. Biomed. Health Inform. 20(5), 1416–1426 (2015)
Chan, C.W., Green, L.V., Lekwijit, S., Lu, L., Escobar, G.: Assessing the impact of service level when customer needs are uncertain: an empirical investigation of hospital step-down units. Manag. Sci. 65(2), 751–775 (2018)
Armony, M., Chan, C.W., Zhu, B.: Critical care capacity management: Understanding the role of a step down unit. Prod. Oper. Manag. 27(5), 859–883 (2018)
Luo, G.: PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Inf. Sci. Syst. 4(1), 5 (2016)
Thabtah, F., Abdelhamid, N., Peebles, D.: A machine learning autism classification based on logistic regression analysis. Health Inf. Sci. Syst. 7(1), 12 (2019)
Hu, W., Chan, C.W., Zubizarreta, J.R., Escobar, G.J.: An examination of early transfers to the ICU based on a physiologic risk score. Manuf. Serv. Oper. Manag. 20(3), 531–549 (2018)
Rudin, C., Ustun, B.: Optimized scoring systems: toward trust in machine learning for healthcare and criminal justice. Interfaces 48(5), 449–466 (2018)
Gao, Y., Xu, A., Hu, P.J.-H., Cheng, T.-H.: Incorporating association rule networks in feature category-weighted naive Bayes model to support weaning decision making. Decis. Support Syst. 96, 27–38 (2017)
Jagadeeswari, V., Subramaniyaswamy, V., Logesh, R., Vijayakumar, V.: A study on medical Internet of Things and big data in personalized healthcare system. Health Inf. Sci. Syst. 6(1), 14 (2018)
Silva, I., Moody, G., Scott, D.J., Celi, L.A., Mark, R.G.: Predicting in-hospital mortality of icu patients: the physionet/computing in cardiology challenge 2012. In: 2012 Computing in Cardiology, pp. 245–248. IEEE (2012)
Abu-Samah, A., Razak, N.N.A., Suhaimi, F.M., Jamaludin, U.K., Chase, G.: Linking Bayesian network and intensive care units data: a glycemic control study. In: TENCON 2018-2018 IEEE Region 10 Conference, pp. 1988–1993. IEEE (2018)
Thanathornwong, B.: Bayesian-based decision support system for assessing the needs for orthodontic treatment. Healthcare Inform. Res. 24(1), 22–28 (2018)
Lin, K., Hu, Y., Kong, G.: Predicting in-hospital mortality of patients with acute kidney injury in the ICU using random forest model. Int. J. Med. Inform. 125, 55–61 (2019)
Ghasemi, P., Raoufy, M.R.: Prediction of mortality in patients with sepsis using detrended fluctuation analysis of heart rate variability. In: 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), pp. 150–154. IEEE (2016)
Li-wei, H.L., et al.: A physiological time series dynamics-based approach to patient monitoring and outcome prediction. IEEE J. Biomed. Health Inform. 19(3), 1068–1076 (2014)
Wong, E.G., Parker, A.M., Leung, D.G., Brigham, E.P., Arbaje, A.I.: Association of severity of illness and intensive care unit readmission: a systematic review. Heart Lung: J. Acute Crit. Care 45(1), 3–9 (2016)
Aminiahidashti, H., Bozorgi, F., Montazer, S.H., Baboli, M., Firouzian, A.: Comparison of APACHE II and SAPS II scoring systems in prediction of critically ill patients’ outcome. Emergency 5(1), 4 (2017)
Wang, X., et al.: A new method to predict hospital mortality in severe community acquired pneumonia. Eur. J. Intern. Med. 40, 56–63 (2017)
Itani, R., Minami, Y., Haruki, S., Watanabe, E., Hagiwara, N.: Prognostic impact of disseminated intravascular coagulation score in acute heart failure patients referred to a cardiac intensive care unit: a retrospective cohort study. Heart Vessels 32(7), 872–879 (2017)
Citi, L., Barbieri, R.: PhysioNet 2012 challenge: predicting mortality of ICU patients using a cascaded SVM-GLM paradigm. In: 2012 Computing in Cardiology, pp. 257–260. IEEE (2012)
Ghosh, S., Nguyen, H., Li, J.: B. T.-I. C. of the I. E. in M. & B. S.: Predicting short-term ICU outcomes using a sequential contrast motif based classification framework (2016)
Acknowledgments
This work is fully supported by the National Natural Science Foundation of China [Nos. 91846107, 71571058, and 71690235], Anhui Provincial Science and Technology Major Project [Nos. 17030801001, and 18030801137], and the Fundamental Research Funds for the Central Universities [No. PA2019GDQT0021].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tan, R., Ding, S., Pan, J., Qiu, Y. (2019). ICU Mortality Prediction Based on Key Risk Factors Identification. In: Wang, H., Siuly, S., Zhou, R., Martin-Sanchez, F., Zhang, Y., Huang, Z. (eds) Health Information Science. HIS 2019. Lecture Notes in Computer Science(), vol 11837. Springer, Cham. https://doi.org/10.1007/978-3-030-32962-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-32962-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32961-7
Online ISBN: 978-3-030-32962-4
eBook Packages: Computer ScienceComputer Science (R0)