References
- Toussaint M, van Hove O, Leduc D, et al. Invasive versus noninvasive paediatric home mechanical ventilation: review of the international evolution over the past 24 years. Thorax. 2024;79(6):581–588.
- Szafran JC, Patel BK. Invasive Mechanical Ventilation. Crit Care Clin. 2024;40(2):255–273.
- Dolinay T, Hsu L, Maller A, et al. Ventilator Weaning in Prolonged Mechanical Ventilation-A Narrative Review. J Clin Med. 2024;13(7):1909.
- Marinaki C, Kapadochos T, Katsoulas T, et al. Estimation of the optimal time needed for weaning of Intensive Care Unit tracheostomized patients on mechanical ventilation. A prospective observational study. Acta Biomed. 2023;94(2):e2023103.
- Pham T, Heunks L, Bellani G, et al. Weaning from mechanical ventilation in intensive care units across 50 countries (WEAN SAFE): a multicentre, prospective, observational cohort study. Lancet Respir Med. 2023;11(5):465–476.
- Boles JM, Bion J, Connors A, et al. Weaning from mechanical ventilation. Eur Respir J. 2007;29(5):1033–1056.
- Varón-Vega F, Giraldo-Cadavid LF, Uribe AM, et al. Utilization of spontaneous breathing trial, objective cough test, and diaphragmatic ultrasound results to predict extubation success: COBRE-US trial. Crit Care. 2023;27(1):414.
- Igarashi Y, Ogawa K, Nishimura K, Osawa S, Ohwada H, Yokobori S. Machine learning for predicting successful extubation in patients receiving mechanical ventilation. Front Med (Lausanne). 2022;9:961252.
- Hur S, Min JY, Yoo J, et al. Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study. J Med Internet Res. 2021;23(8):e23508.
- Liu CF, Hung CM, Ko SC, et al. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne). 2022;9:935366.
- Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J Clin Med. 2024;13(5):1505.
- Chen T, Xu J, Ying H, et al. Prediction of extubation failure for intensive care unit patients using light gradient boosting machine. IEEE Access. 2019;7:150960–8.
- Fabregat A, Magret M, Ferré JA, et al. A Machine Learning decision-making tool for extubation in Intensive Care Unit patients. Comput Methods Programs Biomed. 2021;200:105869.
- Otaguro T, Tanaka H, Igarashi Y, et al. Machine learning for prediction of successful extubation of mechanical ventilated patients in an intensive care unit: a retrospective observational study. J Nippon Med Sch. 2021;88:408–17.
- Zhao QY, Wang H, Luo JC, et al. Development and validation of a machine-learning model for prediction of extubation failure in intensive care units. Front Med. 2021;8:676343.
- Fleuren LM, Dam TA, Tonutti M, et al. Predictors for extubation failure in COVID-19 patients using a machine learning approach. Crit Care. 2021;25:448.
- Thille AW, Gacouin A, Coudroy R, et al. Spontaneous-Breathing Trials with Pressure-Support Ventilation or a T-Piece. N Engl J Med. 2022;387(20):1843–1854.
- Varón-Vega F, Rincón A, Giraldo-Cadavid LF, et al. Assessing the reproducibility and predictive value of objective cough measurement for successful withdrawal of invasive ventilatory support in adult patients. BMC Pulm Med. 2024;24(1):218.
- Goligher EC, Laghi F, Detsky ME, et al. Measuring diaphragm thickness with ultrasound in mechanically ventilated patients: feasibility, reproducibility and validity. Intensive Care Med. 2015;41(4):642–649.
- Vivier E, Muller M, Putegnat JB, et al. Inability of diaphragm ultrasound to predict extubation failure: a multicenter study. Chest. 2019;155(6):1131–1139.
- Matamis D, Soilemezi E, Tsagourias M, et al. Sonographic evaluation of the diaphragm in critically ill patients. Technique and clinical applications. Intensive Care Med. 2013;39(5):801–810.
- Berrar D. Cross-validation. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. Elsevier; 2018. p. 542–5.
- Šimundić AM. Measures of diagnostic accuracy: basic definitions. EJIFCC. 2009;19(4):203–11.
- Hosmer DW, Lemeshow S, Sturdivant RX. Applied logistic regression, 3rd edn. Hosmer DW, Lemeshow S, Sturdivant RX, editors. New York: Wiley; 2013; 2013.
- Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381.
- Chen JH, Asch SM. Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. N Engl J Med. 2017;376(26):2507–2509.
- Shamout F, Zhu T, Clifton DA. Machine Learning for Clinical Outcome Prediction. IEEE Rev Biomed Eng. 2021;14:116–126.
- Huang Y, Guo J, Chen WH, et al. A scoping review of fair machine learning techniques when using real-world data. J Biomed Inform. 2024;151:104622.
- Ono S. Building a better machine learning model of extubation for neurocritical care patients. Intensive Care Med. 2023 Jan;49(1):119–120.
- Park JE, Kim DY, Park JW, et al. Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials. Bioengineering (Basel). 2023;10(10):1163.
- Liao KM, Ko SC, Liu CF, et al. Development of an Interactive AI System for the Optimal Timing Prediction of Successful Weaning from Mechanical Ventilation for Patients in Respiratory Care Centers. Diagnostics (Basel). 2022;12(4):975.
- Maldonado-Franco A, Giraldo-Cadavid LF, Tuta-Quintero E, Cagy M, Bastidas Goyes AR, Botero-Rosas DA. Curve-Modelling and Machine Learning for a Better COPD Diagnosis. Int J Chron Obstruct Pulmon Dis. 2024;19:1333–1343.
- Hudson DL, Cohen ME. Neural networks and artificial intelligence for biomedical engineering. Nueva York, NY, EE. UU.: IEEE; 2000.