ARDS Definition Task Force, Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, Fan E, Camporota L, Slutsky AS (2012) Acute respiratory distress syndrome: the Berlin Definition. JAMA 307(23):2526–33. https://doi.org/10.1001/jama.2012.5669
Matthay MA, Zemans RL, Zimmerman GA, Arabi YM, Beitler JR, Mercat A, Herridge M, Randolph AG, Calfee CS (2019) Acute respiratory distress syndrome. Nat Rev Dis Primers 5(1):18. https://doi.org/10.1038/s41572-019-0069-0
Article PubMed PubMed Central Google Scholar
Ding XF, Li JB, Liang HY, Wang ZY, Jiao TT, Liu Z, Yi L, Bian WS, Wang SP, Zhu X, Sun TW (2019) Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study. J Transl Med 17(1):326. https://doi.org/10.1186/s12967-019-2075-0
Article PubMed PubMed Central Google Scholar
Huang B, Liang D, Zou R, Yu X, Dan G, Huang H, Liu H, Liu Y (2021) Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study. Ann Transl Med 9(9):794. https://doi.org/10.21037/atm-20-6624
Article PubMed PubMed Central Google Scholar
Sayed M, Riaño D, Villar J (2021) Novel criteria to classify ARDS severity using a machine learning approach. Crit Care 25(1):150. https://doi.org/10.1186/s13054-021-03566-w
Article PubMed PubMed Central Google Scholar
Villar J, Slutsky AS (2017) GOLDEN anniversary of the acute respiratory distress syndrome: still much work to do! Curr Opin Crit Care 23(1):4–9. https://doi.org/10.1097/MCC.0000000000000378
Ferring M, Vincent JL (1997) Is outcome from ARDS related to the severity of respiratory failure? Eur Respir J 10(6):1297–1300. https://doi.org/10.1183/09031936.97.10061297
Article CAS PubMed Google Scholar
Cysneiros A, Galvão T, Domingues N, Jorge P, Bento L, Martin-Loeches I (2024) ARDS mortality prediction model using evolving clinical data and chest radiograph analysis. Biomedicines 12(2):439. https://doi.org/10.3390/biomedicines12020439
Article PubMed PubMed Central Google Scholar
Wang YC, Zhang SH, Lv WH, Wang WL, Huang S, Qiu Y, Xie JF, Yang Y, Ju S (2023) Added value of chest CT images to a personalized prognostic model in acute respiratory distress syndrome: a retrospective study. Chin J Acad Radiol 6(1):47–56. https://doi.org/10.1007/s42058-023-00116-x
Article PubMed PubMed Central Google Scholar
Villar J, González-Martín JM, Hernández-González J, Armengol MA, Fernández C, Martín-Rodríguez C, Mosteiro F, Martínez D, Sánchez-Ballesteros J, Ferrando C, Domínguez-Berrot AM, Añón JM, Parra L, Montiel R, Solano R, Robaglia D, Rodríguez-Suárez P, Gómez-Bentolila E, Fernández RL, Szakmany T, Steyerberg EW, Slutsky AS, Predicting Outcome and Stratification of severity in ARDS (POSTCARDS) Network (2023) Predicting ICU mortality in acute respiratory distress syndrome patients using machine learning: the predicting outcome and STratifiCation of severity in ARDS (POSTCARDS) study. Crit Care Med 51(12):1638–1649. https://doi.org/10.1097/CCM.0000000000006030
Zhang GH, Zhang HM, Fang MX, Zhang Q, Ding RS (2023) An interpretability approach for mortality risk prediction based on W-BDA and MLP. UPB Sci Bull 85:246
Tang R, Tang W, Wang D (2022) Predictive value of machine learning for in-hospital mortality for trauma-induced acute respiratory distress syndrome patients: an analysis using the data from MIMIC III. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 34(3):260–264. https://doi.org/10.3760/cma.j.cn121430-20211117-01741
Hu J, Fei Y, Li WQ (2022) Predicting the mortality risk of acute respiratory distress syndrome: radial basis function artificial neural network model versus logistic regression model. J Clin Monit Comput 36(3):839–848. https://doi.org/10.1007/s10877-021-00716-x
Zhang Z (2019) Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model. PeerJ 7:e7719. https://doi.org/10.7717/peerj.7719
Article PubMed PubMed Central Google Scholar
Tran TK, Tran MC, Joseph A, Phan PA, Grau V, Farmery AD (2024) A systematic review of machine learning models for management, prediction and classification of ARDS. Respir Res 25(1):232. https://doi.org/10.1186/s12931-024-02834-x
Article PubMed PubMed Central Google Scholar
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM, QUADAS-2 Group (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–36. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
Chu H, Cole SR (2006) Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol 59(12):1331–1332. https://doi.org/10.1016/j.jclinepi.2006.06.011
Wang Z, Xing L, Cui H, Fu G, Zhao H, Huang M, Zhao Y, Xu J (2022) A nomogram for predicting the mortality of patients with acute respiratory distress syndrome. J Healthc Eng 2022:5940900. https://doi.org/10.1155/2022/5940900
Article PubMed PubMed Central Google Scholar
Lynam AL, Dennis JM, Owen KR, Oram RA, Jones AG, Shields BM, Ferrat LA (2020) Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults. Diagn Progn Res 4:6. https://doi.org/10.1186/s41512-020-00075-2
Article PubMed PubMed Central Google Scholar
Shariati MM, Eslami S, Shoeibi N, Eslampoor A, Sedaghat M, Gharaei H, Zarei-Ghanavati S, Derakhshan A, Abrishami M, Abrishami M, Hosseini SM, Rad SS, Astaneh MA, Farimani RM (2024) Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches. BMC Med Inform Decis Mak 24(1):131. https://doi.org/10.1186/s12911-024-02520-4
Article PubMed PubMed Central Google Scholar
Charilaou P, Battat R (2022) Machine learning models and over-fitting considerations. World J Gastroenterol 28(5):605–607. https://doi.org/10.3748/wjg.v28.i5.605
Article PubMed PubMed Central Google Scholar
Hosseinzadeh M, Gorji A, Fathi Jouzdani A, Rezaeijo SM, Rahmim A, Salmanpour MR (2023) Prediction of cognitive decline in parkinson’s disease using clinical and DAT spect imaging features, and hybrid machine learning systems. Diagnostics 13(10):1691. https://doi.org/10.3390/diagnostics13101691
Article PubMed PubMed Central Google Scholar
Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA, NHLBI ARDS Network (2014) Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med 2(8):611–20. https://doi.org/10.1016/S2213-2600(14)70097-9
Article PubMed PubMed Central Google Scholar
Famous KR, Delucchi K, Ware LB, Kangelaris KN, Liu KD, Thompson BT, Calfee CS; ARDS Network. Acute Respiratory Distress Syndrome Subphenotypes Respond Differently to Randomized Fluid Management Strategy. Am J Respir Crit Care Med. 2017; 195(3): 331–338. https://doi.org/10.1164/rccm.201603-0645OC. Erratum in: Am J Respir Crit Care Med. 2018; 198(12): 1590. Erratum in: Am J Respir Crit Care Med. 2019;200(5):649. PMID: 27513822; PMCID: PMC5328179.
Calfee CS, Delucchi KL, Sinha P, Matthay MA, Hackett J, Shankar-Hari M, McDowell C, Laffey JG, O’Kane CM, McAuley DF, Irish Critical Care Trials Group (2018) Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med 6(9):691–698. https://doi.org/10.1016/S2213-2600(18)30177-2
Article CAS PubMed PubMed Central Google Scholar
Sinha P, Delucchi KL, Thompson BT, McAuley DF, Matthay MA, Calfee CS, NHLBI ARDS Network (2018) Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study. Intensive Care Med 44(11):1859–1869. https://doi.org/10.1007/s00134-018-5378-3
Article CAS PubMed PubMed Central Google Scholar
Sinha P, Delucchi KL, McAuley DF, O’Kane CM, Matthay MA, Calfee CS (2020) Development and validation of parsimonious algorithms to classify acute respiratory distress syndrome phenotypes: a secondary analysis of randomised controlled trials. Lancet Respir Med 8(3):247–257. https://doi.org/10.1016/S2213-2600(19)30369-8
Article CAS PubMed PubMed Central Google Scholar
Zhang Z, Zhang G, Goyal H, Mo L, Hong Y (2018) Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis. Crit Care 22(1):347.
留言 (0)