| 1 |
罗亮, 宋勇. 急性呼吸窘迫综合征临床进展[M]. 北京: 人民卫生出版社, 2017.
|
| 2 |
Bernard GR, Artigas A, Brigham KL, et al. Report of the American-European consensus conference on ARDS: definitions, mechanisms, relevant outcomes and clinical trial coordination. The Consensus Committee [J]. Intensive Care Med, 1994, 20(3): 225-232.
|
| 3 |
Force ADT, Ranieri VM, Rubenfeld GD, et al. Acute respiratory distress syndrome: the Berlin Definition [J]. JAMA, 2012, 307(23): 2526-2533.
|
| 4 |
Henderson WR, Chen L, Amato MBP, et al. Fifty years of research in ARDS. Respiratory mechanics in acute respiratory distress syndrome [J]. Am J Respir Crit Care Med, 2017, 196(7): 822-833.
|
| 5 |
Reilly JP, Calfee CS, Christie JD. Acute respiratory distress syndrome phenotypes [J]. Semin Respir Crit Care Med, 2019, 40(1): 19-30.
|
| 6 |
Bhattarai S, Gupta A, Ali E, et al. Can big data and machine learning improve our understanding of acute respiratory distress syndrome? [J]. Cureus, 2021, 13(2): e13529.
|
| 7 |
Ashbaugh DG, Bigelow DB, Petty TL, et al. Acute respiratory distress in adults [J]. Lancet, 1967, 2(7511): 319-323.
|
| 8 |
Bernard GR, Artigas A, Brigham KL, et al. The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination [J]. Am J Respir Crit Care Med, 1994, 149(3 Pt 1): 818-824.
|
| 9 |
Villar J, Schultz MJ, Kacmarek RM. The LUNG SAFE: a biased presentation of the prevalence of ARDS! [J]. Crit Care, 2016, 20(1): 108.
|
| 10 |
Bellani G, Laffey JG, Pham T, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries [J]. JAMA, 2016, 315(8): 788-800.
|
| 11 |
Sayed M, Riaño D, Villar J. Novel criteria to classify ARDS severity using a machine learning approach [J]. Crit Care, 2021, 25(1): 150.
|
| 12 |
Tong A, Elliott JH, Azevedo LC, et al. Core outcomes set for trials in people with coronavirus disease 2019 [J]. Crit Care Med, 2020, 48(11): 1622-1635.
|
| 13 |
Maddali MV, Churpek M, Pham T, et al. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis [J]. Lancet Respir Med, 2022, 10(4): 367-377.
|
| 14 |
Sinha P, Delucchi KL, Chen Y, et al. Latent class analysis-derived subphenotypes are generalisable to observational cohorts of acute respiratory distress syndrome: a prospective study [J]. Thorax, 2022, 77(1): 13-21.
|
| 15 |
Calfee CS, Delucchi K, Parsons PE, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials [J]. Lancet Respir Med, 2014, 2(8): 611-620.
|
| 16 |
Famous KR, Delucchi K, Ware LB, et al. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy [J]. Am J Respir Crit Care Med, 2017, 195(3): 331-338.
|
| 17 |
Calfee CS, Delucchi KL, Sinha P, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial [J]. Lancet Respir Med, 2018, 6(9): 691-698.
|
| 18 |
Zhang L, Wang DC, Huang Q, et al. Significance of clinical phenomes of patients with COVID-19 infection: a learning from 3795 patients in 80 reports [J]. Clin Transl Med, 2020, 10: 8.
|
| 19 |
Moss M, Ulysse CA, Angus DC, et al. Early neuromuscular blockade in the acute respiratory distress syndrome. Reply [J]. N Engl J Med, 2019, 381(8): 787-788.
|
| 20 |
Reamaroon N, Sjoding MW, Gryak J, et al. Automated detection of acute respiratory distress syndrome from chest X-rays using directionality measure and deep learning features [J]. Comput Biol Med, 2021, 134: 104463.
|
| 21 |
Herrmann P, Busana M, Cressoni M, et al. Using artificial intelligence for automatic segmentation of CT lung images in acute respiratory distress syndrome [J]. Front Physiol, 2021, 12: 676118.
|
| 22 |
Farzaneh N, Ansari S, Lee E, et al. Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome [J]. NPJ Digit Med, 2023, 6(1): 62.
|
| 23 |
Kang M, Hong KS, Chikontwe P, et al. Quantitative assessment of chest CT patterns in COVID-19 and bacterial pneumonia patients: a deep learning perspective [J]. J Korean Med Sci, 2021, 36(5): e46.
|
| 24 |
Huang L, Han R, Ai T, et al. Serial quantitative chest CT assessment of COVID-19: a deep learning approach [J]. Radiol Cardiothorac Imaging, 2020, 2(2): e200075.
|
| 25 |
Lanza E, Muglia R, Bolengo I, et al. Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation [J]. Eur Radiol, 2020, 30(12): 6770-6778.
|
| 26 |
Wang Y, Chen Y, Wei Y, et al. Quantitative analysis of chest CT imaging findings with the risk of ARDS in COVID-19 patients: a preliminary study [J]. Ann Transl Med, 2020, 8(9): 594.
|
| 27 |
Kirby RR, Downs JB, Civetta JM, et al. High level positive end expiratory pressure (PEEP) in acute respiratory insufficiency [J]. Chest, 1975, 67(2): 156-163.
|
| 28 |
Gattinoni L, Meissner K, Marini JJ. The baby lung and the COVID-19 era [J]. Intensive Care Med, 2020, 46(7): 1438-1440.
|
| 29 |
Brusasco C, Santori G, Tavazzi G, et al. Second-order grey-scale texture analysis of pleural ultrasound images to differentiate acute respiratory distress syndrome and cardiogenic pulmonary edema [J]. J Clin Monit Comput, 2022, 36(1): 131-140.
|
| 30 |
Bataille B, Riu B, Ferre F, et al. Integrated use of bedside lung ultrasound and echocardiography in acute respiratory failure: a prospective observational study in ICU [J]. Chest, 2014, 146(6): 1586-1593.
|
| 31 |
Uckun S, Dawant BM, Lindstrom DP. Model-based diagnosis in intensive care monitoring: the YAQ approach [J]. Artif Intell Med, 1993, 5(1): 31-48.
|
| 32 |
Hagan R, Gillan CJ, Spence I, et al. Comparing regression and neural network techniques for personalized predictive analytics to promote lung protective ventilation in Intensive Care Units [J]. Comput Biol Med, 2020, 126: 104030.
|
| 33 |
Bottino DA, Giannella-Neto A, David CM, et al. Decision support system to assist mechanical ventilation in the adult respiratory distress syndrome [J]. Int J Clin Monit Comput, 1997, 14(2): 73-81.
|
| 34 |
Ganzert S, Kramer S, Guttmann J. Predicting the lung compliance of mechanically ventilated patients via statistical modeling [J]. Physiol Meas, 2012, 33(3): 345-359.
|
| 35 |
Perchiazzi G, Giuliani R, Ruggiero L, et al. Estimating respiratory system compliance during mechanical ventilation using artificial neural networks [J]. Anesth Analg, 2003, 97(4): 1143-1148.
|
| 36 |
Schultz MJ. Lung-protective mechanical ventilation with lower tidal volumes in patients not suffering from acute lung injury: a review of clinical studies [J]. Med Sci Monit, 2008, 14(2): RA22-26.
|
| 37 |
Ma J, Li Q, Ji D, et al. Predicting candidate therapeutic drugs for sepsis-induced acute respiratory distress syndrome based on transcriptome profiling [J]. Bioengineered, 2021, 12(1): 1369-1380.
|
| 38 |
Artigas L, Coma M, Matos-Filipe P, et al. In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm [J]. PLoS One, 2020, 15(10): e0240149.
|
| 39 |
Segú-Vergés C, Artigas L, Coma M, et al. Artificial intelligence assessment of the potential of tocilizumab along with corticosteroids therapy for the management of COVID-19 evoked acute respiratory distress syndrome [J]. PLoS One, 2023, 18(2): e0280677.
|