Methods and measures to quantify ICU patient heterogeneity
Tài liệu tham khảo
Lenert, 2019, A method for analyzing inpatient care variability through physicians’ orders, J. Biomed. Inform., 91, 103111, 10.1016/j.jbi.2019.103111
Ruyssinck, 2016, Random survival forests for predicting the bed occupancy in the intensive care unit, Comput. Math. Methods Med., 1, 10.1155/2016/7087053
Bing-Hua, 2014, Delayed admission to intensive care unit for critically surgical patients is associated with increased mortality, Am. J. Surgery, 208, 268, 10.1016/j.amjsurg.2013.08.044
Droogh, 2015, Transferring the critically ill patient: are we there yet?, Crit. Care, 19, 62, 10.1186/s13054-015-0749-4
Awad, 2017, Patient length of stay and mortality prediction: A survey, Health Services Manage. Res., 30, 105, 10.1177/0951484817696212
Verburg, 2017, Which models can i use to predict adult ICU length of stay? A systematic review, Crit. Care Med., 45, e222, 10.1097/CCM.0000000000002054
Peres, 2020, What factors predict length of stay in the intensive care unit?, J. Crit. Care, 60, 183, 10.1016/j.jcrc.2020.08.003
G. Gutierrez, Artificial intelligence in the intensive care unit, Crit. Care 24 (101) (2020).
Temple, 2015, Predicting discharge dates from the NICU using progress note data, Pediatrics, 136, e395, 10.1542/peds.2015-0456
D. Cuadrado, D. Riaño, J. Gómez, M. Bodí, G. Sirgo, F. Esteban, R. Rodríguez, Pursuing optimal prediction of discharge time in ICUs with machine learning methods, in: Artificial Intelligence in Medicine, Vol. 11526, Springer, 2019, pp. 150–154.
Artis, 2019, Data omission by physician trainees on ICU rounds, Crit. Care Med., 47, 403, 10.1097/CCM.0000000000003557
Sirgo, 2018, Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment, Int. J. Med. Informatics, 112, 166, 10.1016/j.ijmedinf.2018.02.007
Caruana, 2015, Longitudinal studies, J. Thorac. Dis., 7, E537
Moreno, 1999, The use of maximum SOFA score to quantify organ dysfunction/failure in intensive care. results of a prospective, multicentre study. working group on sepsis related problems of the esicm, Intensive Care Med., 25, 686, 10.1007/s001340050931
S. Lambden, P.F. Laterre, M.M. Levy, B. Francois, The SOFA score-development, utility and challenges of accurate assessment in clinical trials, Crit Care 23 (1) (2019).
D.R. Miranda, R. Nap, A. de Rijk, W. Schaufeli, L. G, Therapeutic intervention scoring system. Nursing Activities Score, Crit. Care Med. 31 (2003) 374–382.
Roca-Biosca, 2015, Validation of EMINA and EVARUCI scales for assessing the risk of developing pressure ulcers in critical patients (in spanish), Enfermería Intensiva, 26, 15, 10.1016/j.enfi.2014.10.003
Davies, 1979, A cluster separation measure, IEEE Trans. Pattern Anal. Mach. Intell., 1, 224, 10.1109/TPAMI.1979.4766909
Dunn, 1974, Well-separated clusters and optimal fuzzy partitions, J. Cybernet., 4, 95, 10.1080/01969727408546059
Rousseeuw, 1987, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53, 10.1016/0377-0427(87)90125-7
Halkidi, 2001, On clustering validation techniques, J. Intell. Inf. Syst., 17, 107, 10.1023/A:1012801612483
Pakhira, 2004, Validity index for crisp and fuzzy clusters, Pattern Recogn., 37, 487, 10.1016/j.patcog.2003.06.005
L. Kaufman, P.J. Rousseeuw, Finding Groups in Data. An Introduction to Cluster Analysis, John Wiley and Sons, 1990.
Cosgriff, 2019, Critical care, critical data, Biomed. Eng. Comput. Biol., 10, 10.1177/1179597219856564
Vranas, 2017, Identifying distinct subgroups of ICU patients: A machine learning approach, Crit. Care Med., 45, 1607, 10.1097/CCM.0000000000002548
J.A. Silva, E.R. Faria, R.C. Barros, E.R. Hruschka, A.C.P.L.F. d. Carvalho, J. a. Gama, Data stream clustering: A survey, ACM Comput. Surv. 46 (1). doi:10.1145/2522968.2522981.
Aghabozorgi, 2015, Time-series clustering – a decade review, Inf. Syst., 53, 16, 10.1016/j.is.2015.04.007