Journal of Peking University (Health Sciences) ›› 2021, Vol. 53 ›› Issue (3): 566-572. doi: 10.19723/j.issn.1671-167X.2021.03.021
Previous Articles Next Articles
LIN Yu1,2,WU Jing-yi3,LIN Ke1,2,HU Yong-hua2,4,KONG Gui-lan1,3,Δ()
CLC Number:
[1] |
Halpern NA, Pastores SM. Critical care medicine in the United States 2000-2005: an analysis of bed numbers, occupancy rates, payer mix, and costs[J]. Crit Care Med, 2010,38(1):65-71.
doi: 10.1097/CCM.0b013e3181b090d0 |
[2] |
Woldhek AL, Rijkenberg S, Bosman RJ, et al. Readmission of ICU patients: A quality indicator?[J]. J Crit Care, 2017,38:328-334.
doi: 10.1016/j.jcrc.2016.12.001 |
[3] |
Kramer AA, Higgins TL, Zimmerman JE. The association between ICU readmission rate and patient outcomes[J]. Crit Care Med, 2013,41(1):24-33.
doi: 10.1097/CCM.0b013e3182657b8a |
[4] |
Rosenberg AL, Hofer TP, Hayward RA, et al. Who bounces back? Physiologic and other predictors of intensive care unit readmission[J]. Crit Care Med, 2001,29(3):511-518.
pmid: 11373413 |
[5] |
Baker DR, Pronovost PJ, Morlock LL, et al. Patient flow variabi-lity and unplanned readmissions to an intensive care unit[J]. Crit Care Med, 2009,37(11):2882-2887.
doi: 10.1097/CCM.0b013e3181b01caf |
[6] |
Martin LA, Kilpatrick JA, Al-Dulaimi R, et al. Predicting ICU readmission among surgical ICU patients: Development and validation of a clinical nomogram[J]. Surgery, 2019,165(2):373-380.
doi: S0039-6060(18)30429-X pmid: 30170817 |
[7] |
Lee H, Lim CW, Hong HP, et al. Efficacy of the APACHE Ⅱ score at ICU discharge in predicting post-ICU mortality and ICU readmission in critically ill surgical patients[J]. Anaesth Intensive Care, 2015,43(2):175-186.
doi: 10.1177/0310057X1504300206 |
[8] |
Fialho AS, Cismondi F, Vieira SM, et al. Data mining using clinical physiology at discharge to predict ICU readmissions[J]. Expert Syst Appl, 2012,39(18):13158-13165.
doi: 10.1016/j.eswa.2012.05.086 |
[9] |
Desautels T, Das R, Calvert J, et al. Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach[J]. BMJ Open, 2017,7(9):e017199.
doi: 10.1136/bmjopen-2017-017199 |
[10] |
Hosni M, Abnane I, Idri A, et al. Reviewing ensemble classification methods in breast cancer[J]. Comput Methods Programs Biomed, 2019,177:89-112.
doi: 10.1016/j.cmpb.2019.05.019 |
[11] | Liu Y, Gu Y, Nguyen JC, et al. Symptom severity classification with gradient tree boosting[J]. J Biomed Inform, 2017,75S:S105-S111. |
[12] |
Johnson AE, Pollard TJ, Shen L, et al. MIMIC-Ⅲ, a freely accessible critical care database[J]. Sci Data, 2016,3:160035.
doi: 10.1038/sdata.2016.35 |
[13] |
Austin SR, Wong YN, Uzzo RG, et al. Why summary comorbidity measures such as the Charlson comorbidity index and Elixhauser score work[J]. Med Care, 2015,53(9):E65-E72.
doi: 10.1097/MLR.0b013e318297429c |
[14] |
Oakes DF, Borges IN, Forgiarini Junior LA, et al. Assessment of ICU readmission risk with the stability and workload index for transfer score[J]. J Bras Pneumol, 2014,40(1):73-76.
doi: 10.1590/S1806-37132014000100011 |
[15] |
Xue Y, Klabjan D, Luo Y. Predicting ICU readmission using grouped physiological and medication trends[J]. Artif Intell Med, 2019,95:27-37.
doi: S0933-3657(17)30648-6 pmid: 30213670 |
[16] |
He HB, Garcia EA. Learning from imbalanced data[J]. IEEE T Knowl Data En, 2009,21(9):1263-1284.
doi: 10.1109/TKDE.2008.239 |
[17] |
Rahman R, Matlock K, Ghosh S, et al. Heterogeneity aware random forest for drug sensitivity prediction[J]. Sci Rep, 2017,7(1):11347.
doi: 10.1038/s41598-017-11665-4 |
[18] |
Hu J. Automated detection of driver fatigue based on AdaBoost classifier with EEG signals[J]. Front Comput Neurosci, 2017,11:72.
doi: 10.3389/fncom.2017.00072 |
[19] |
Friedman JH. Greedy function approximation: A gradient boosting machine[J]. Ann Stat, 2001,29(5):1189-1232.
doi: 10.1214/aos/1013203450 |
[20] | Mani I, Zhang I. kNN approach to unbalanced data distributions: a case study involving information extraction[C]// ICML 2003 Workshop on Learning from Imbalanced Datasets, August 21-24, 2003. Washington, D.C.: ICML, 2003. |
[1] | WU Jing-yi,LIN Yu,LIN Ke,HU Yong-hua,KONG Gui-lan. Predicting prolonged length of intensive care unit stay via machine learning [J]. Journal of Peking University (Health Sciences), 2021, 53(6): 1163-1170. |
[2] | Xue-hua ZHU,Ming-yu YANG,Hai-zhui XIA,Wei HE,Zhi-ying ZHANG,Yu-qing LIU,Chun-lei XIAO,Lu-lin MA,Jian LU. Application of machine learning models in predicting early stone-free rate after flexible ureteroscopic lithotripsy for renal stones [J]. Journal of Peking University(Health Sciences), 2019, 51(4): 653-659. |
[3] | YANG Cheng, ZHANG Yu-qi, TANG Xun, GAO Pei, WEI Chen-lu, HU Yong-hua. Retrospective cohort study for the impact on readmission of patients with ischemic stroke after treatment of aspirin plus clopidogrel or aspirin mono-therapy [J]. Journal of Peking University(Health Sciences), 2016, 48(3): 442-447. |
|