{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T14:21:58Z","timestamp":1788877318052,"version":"build-2803163510"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Traditional assessment of the readiness for the weaning from the mechanical ventilator (MV) needs respiratory parameters in a spontaneous breath. Exempted from the MV disconnecting and manual measurements of weaning parameters, a prediction model based on parameters from MV and electronic medical records (EMRs) may help the assessment before spontaneous breath trials. The study aimed to develop prediction models using machine learning techniques with parameters from the ventilator and EMRs for predicting successful ventilator mode shifting in the medical intensive care unit. Materials and Methods: A retrospective analysis of 1483 adult patients with mechanical ventilators for acute respiratory failure in three medical intensive care units between April 2015 and October 2017 was conducted by machine learning techniques to establish the predicting models. The input candidate parameters included ventilator setting and measurements, patients\u2019 demographics, arterial blood gas, laboratory results, and vital signs. Several classification algorithms were evaluated to fit the models, including Lasso Regression, Ridge Regression, Elastic Net, Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Machine, and Artificial Neural Network according to the area under the Receiver Operating Characteristic curves (AUROC). Results: Two models were built to predict the success shifting from full to partial support ventilation (WPMV model) or from partial support to the T-piece trial (sSBT model). In total, 3 MV and 13 nonpulmonary features were selected for the WPMV model with the XGBoost algorithm. The sSBT model was built with 8 MV and 4 nonpulmonary features with the Random Forest algorithm. The AUROC of the WPMV model and sSBT model were 0.76 and 0.79, respectively. Conclusions: The weaning predictions using machine learning and parameters from MV and EMRs have acceptable performance. Without manual measurements, a decision-making system would be feasible for the continuous prediction of mode shifting when the novel models process real-time data from MV and EMRs.<\/jats:p>","DOI":"10.3390\/medicina58030360","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T21:25:14Z","timestamp":1646169914000},"page":"360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["The Feasibility of a Machine Learning Approach in Predicting Successful Ventilator Mode Shifting for Adult Patients in the Medical Intensive Care Unit"],"prefix":"10.3390","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8166-2011","authenticated-orcid":false,"given":"Kuang-Hua","family":"Cheng","sequence":"first","affiliation":[{"name":"Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei 10002, Taiwan"},{"name":"Department of Critical Care Medicine, MacKay Memorial Hospital, Taipei 10449, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei-Chu","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Respiratory Therapy, MacKay Memorial Hospital, Taipei 10449, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Jen","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Respiratory Therapy, MacKay Memorial Hospital, Taipei 10449, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Software Product Center, Wistron Corporation, New Taipei City 22175, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Han","family":"Lin","sequence":"additional","affiliation":[{"name":"Software Product Center, Wistron Corporation, New Taipei City 22175, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzu-Min","family":"Chang","sequence":"additional","affiliation":[{"name":"Software Product Center, Wistron Corporation, New Taipei City 22175, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Kuo","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine, MacKay Memorial Hospital, Taipei 10449, Taiwan"},{"name":"Department of Medicine, Mackay Medical College, New Taipei City 25243, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s00134-002-1563-4","article-title":"Post-extubation stridor in intensive care unit patients. Risk factors evaluation and importance of the cuff-leak test","volume":"29","author":"Jaber","year":"2003","journal-title":"Intensive Care Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/S0272-5231(03)00048-0","article-title":"Long-term complications of artificial airways","volume":"24","author":"Sue","year":"2003","journal-title":"Clin. Chest Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1378\/chest.127.3.984","article-title":"Tracheomalacia and tracheobronchomalacia in children and adults: An in-depth review","volume":"127","author":"Carden","year":"2005","journal-title":"Chest"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.ijge.2011.09.021","article-title":"The outcome and predictors of failed extubation in intensive care patients\u2014The elderly is an important predictor","volume":"5","author":"Cheng","year":"2011","journal-title":"Int. J. Gerontol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1056\/NEJM199105233242101","article-title":"A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation","volume":"324","author":"Yang","year":"1991","journal-title":"New Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1183\/09031936.00010206","article-title":"Weaning from mechanical ventilation","volume":"29","author":"Boles","year":"2007","journal-title":"Eur. Respir. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1186\/cc9296","article-title":"Clinical review: The ABC of weaning failure--a structured approach","volume":"14","author":"Heunks","year":"2010","journal-title":"Crit. Care"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.4187\/respcare.03648","article-title":"Improvement in the Prediction of Ventilator Weaning Outcomes by an Artificial Neural Network in a Medical ICU","volume":"60","author":"Kuo","year":"2015","journal-title":"Respir. Care"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s42242-018-0030-1","article-title":"The efficacy and effectiveness of machine learning for weaning in mechanically ventilated patients at the intensive care unit: A systematic review","volume":"2","author":"Kwong","year":"2019","journal-title":"Bio-Des. Manuf."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10877-005-2221-5","article-title":"Weaning from long-term mechanical ventilation: A nonpulmonary weaning index","volume":"18","author":"Todorova","year":"2004","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/j.rmed.2009.02.005","article-title":"Predictors of successful weaning from prolonged mechanical ventilation in Taiwan","volume":"103","author":"Wu","year":"2009","journal-title":"Respir. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1177\/0885066616648133","article-title":"Can creatinine height index predict weaning and survival outcomes in patients on prolonged mechanical ventilation after critical illness?","volume":"33","author":"Datta","year":"2018","journal-title":"J. Intensive Care Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1164\/ajrccm.159.2.9803106","article-title":"Effect of spontaneous breathing trial duration on outcome of attempts to discontinue mechanical ventilation","volume":"159","author":"Esteban","year":"1999","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"400S","DOI":"10.1378\/chest.120.6_suppl.400S","article-title":"Predicting success in weaning from mechanical ventilation","volume":"120","author":"Meade","year":"2001","journal-title":"Chest"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e17392","DOI":"10.1097\/MD.0000000000017392","article-title":"Predicting weaning difficulty for planned extubation patients with an artificial neural network","volume":"98","author":"Hsieh","year":"2019","journal-title":"Medicine"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5587","DOI":"10.1109\/IEMBS.2006.259440","article-title":"Support vector machine classification applied on weaning trials patients","volume":"2006","author":"Giraldo","year":"2006","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1088\/0967-3334\/31\/7\/008","article-title":"Patients on weaning trials classified with support vector machines","volume":"31","author":"Garde","year":"2010","journal-title":"Physiol. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1164\/rccm.201602-0320OC","article-title":"Epidemiology of Weaning Outcome according to a New Definition. The WIND Study","volume":"195","author":"Beduneau","year":"2017","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"54","DOI":"10.4037\/ajcc2006.15.1.54","article-title":"Weaning readiness and fluid balance in older critically ill surgical patients","volume":"15","author":"Epstein","year":"2006","journal-title":"Am. J. Crit. Care"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1097\/01.CCM.0000249828.81705.65","article-title":"Effect of acute kidney injury on weaning from mechanical ventilation in critically ill patients","volume":"35","author":"Vieira","year":"2007","journal-title":"Crit. Care Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lai, Y.-C., Ruan, S.-Y., Huang, C.-T., Kuo, P.-H., and Yu, C.-J. (2013). Hemoglobin levels and weaning outcome of mechanical ventilation in difficult-to-wean patients: A retrospective cohort study. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0073743"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2110-5820-4-2","article-title":"Mechanical ventilation and clinical practice heterogeneity in intensive care units: A multicenter case-vignette study","volume":"4","author":"Nguyen","year":"2014","journal-title":"Ann. Intensive Care"}],"container-title":["Medicina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1648-9144\/58\/3\/360\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:30:00Z","timestamp":1760135400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1648-9144\/58\/3\/360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":24,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["medicina58030360"],"URL":"https:\/\/doi.org\/10.3390\/medicina58030360","relation":{},"ISSN":["1648-9144"],"issn-type":[{"value":"1648-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,1]]}}}