Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters

Huang, Ke; Hsu, Ying-Lin; Chen, Huang‐Chi; Horng, Ming-Hwarng; Chung, Che-Liang; Lin, Ching‐Hsiung; Xu, Jia-Lang; Hou, Ming-Hon

doi:10.3389/fmed.2023.1167445

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Hung [ 55 ] developed a real-time AI model for predicting successful extubation using only six ventilator-derived features. This random forest model exhibited a strong predictive performance, with an AUROC of 0.976.…”

Section: Ai/ml In MV Weaning Predictionmentioning

confidence: 99%

Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

Stivi,

Padawer,

Dirini

et al. 2024

JCM

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The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient’s MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.

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Section: Ai/ml In MV Weaning Predictionmentioning

confidence: 99%

Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

Stivi,

Padawer,

Dirini

et al. 2024

JCM

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Research Progress on Predictive Indictors of Weaning Outcomes in Patients with Invasive Mechanical Ventilation

赵

2024

ACM

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Applying artificial intelligence to predict falls for inpatient

Chen,

2023

Front. Med.

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ObjectiveFalls are adverse events which commonly occur in hospitalized patients. Inpatient falls may cause bruises or contusions and even a fractures or head injuries, which can lead to significant physical and economic burdens for patients and their families. Therefore, it is important to predict the risks involved surrounding hospitalized patients falling in order to better provide medical personnel with effective fall prevention measures.SettingThis study retrospectively used EHR data taken from the Taichung Veterans General Hospital clinical database between January 2015 and December 2019.ParticipantsA total of 53,122 patient records were collected in this study, of which 1,157 involved fall patients and 51,965 were non-fall patients.Primary and secondary outcome measureThis study integrated the characteristics and clinical data of patients with falls and without falls using RapidMiner Studio as an analysis tool for various models of artificial intelligence. Utilization of 8 differ models to identify the most important factors surrounding inpatient fall risk. This study used the sensitivity, specificity, and area under the ROC curve to compute the data by 5-fold cross-validation and then compared them by pairwise t-tests.ResultsThe predictive classifier was developed based upon the gradient boosted trees (XGBoost) model which outperformed the other seven baseline models and achieved a cross-validated ACC of 95.11%, AUC of 0.990, F1 score of 95.1%. These results show that the XGBoost model was used when dealing with multisource patient data, which in this case delivered a highly predictive performance on the risk of inpatient falls.ConclusionMachine learning methods identify the most important factors regarding the detection of inpatients who are at risk of falling, which in turn would improve the quality of patient care and reduce the workloads of the nursing staff when making fall assessments.

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Developing a machine-learning model for real-time prediction of successful extubation in mechanically ventilated patients using time-series ventilator-derived parameters

Cited by 3 publications

References 42 publications

Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome

Research Progress on Predictive Indictors of Weaning Outcomes in Patients with Invasive Mechanical Ventilation

Applying artificial intelligence to predict falls for inpatient

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