Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning

Sayed, Mohammed E.; Riaño, David; Villar, Jesús

doi:10.3390/jcm10173824

Cited by 24 publications

(25 citation statements)

References 32 publications

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“…Unfortunately, an accurate prediction of mechanical ventilation is quite difficult for critical care physicians according to clinical data at admission. A possible solution may be the application of a machine learning technique according to the clinical features of ARDS evaluated in the first days of intensive care admission [ 11 ].…”

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confidence: 99%

Risk Factors and Mortality in Elderly ARDS COVID-19 Compared to Patients without COVID-19

Chiumello

Modafferi

Fratti

2022

JCM

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confidence: 99%

Risk Factors and Mortality in Elderly ARDS COVID-19 Compared to Patients without COVID-19

Chiumello

Modafferi

Fratti

2022

JCM

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“…LightGBM is a type of modified gradient boosting algorithm which overcomes the unsatisfactory efficiency and scalability of traditional gradient boosting algorithms, such as XGBoost (22). Several studies have demonstrated that it has a favorable prediction value in the field of medicine (23)(24)(25). In this study, we found that LightGBM had the best prediction value compared with XGBoost, LR, naïve Bayes and etc.…”

Section: Discussionmentioning

confidence: 53%

A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Congestive Heart Failure

Peng

Wang

et al. 2022

Front. Cardiovasc. Med.

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BackgroundMachine learning (ML) has been used to build high performance prediction model. Patients with congestive heart failure (CHF) are vulnerable to acute kidney injury (AKI) which makes treatment difficult. We aimed to establish an ML-based prediction model for the early identification of AKI in patients with CHF.MethodsPatients data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database, and patients with CHF were selected. Comparisons between several common ML classifiers were conducted to select the best prediction model. Recursive feature elimination (RFE) was used to select important prediction features. The model was improved using hyperparameters optimization (HPO). The final model was validated using an external validation set from the eICU Collaborative Research Database. The area under the receiver operating characteristic curve (AUROC), accuracy, calibration curve and decision curve analysis were used to evaluate prediction performance. Additionally, the final model was used to predict renal replacement therapy (RRT) requirement and to assess the short-term prognosis of patients with CHF. Finally, a software program was developed based on the selected features, which could intuitively report the probability of AKI.ResultsA total of 8,580 patients with CHF were included, among whom 2,364 were diagnosed with AKI. The LightGBM model showed the best prediction performance (AUROC = 0.803) among the 13 ML-based models. After RFE and HPO, the final model was established with 18 features including serum creatinine (SCr), blood urea nitrogen (BUN) and urine output (UO). The prediction performance of LightGBM was better than that of measuring SCr, UO or SCr combined with UO (AUROCs: 0.809, 0.703, 0.560 and 0.714, respectively). Additionally, the final model could accurately predict RRT requirement in patients with (AUROC = 0.954). Moreover, the participants were divided into high- and low-risk groups for AKI, and the 90-day mortality in the high-risk group was significantly higher than that in the low-risk group (log-rank p < 0.001). Finally, external validation using the eICU database comprising 9,749 patients with CHF revealed satisfactory prediction outcomes (AUROC = 0.816).ConclusionA prediction model for AKI in patients with CHF was established based on LightGBM, and the prediction performance of this model was better than that of other models. This model may help in predicting RRT requirement and in identifying the population with poor prognosis among patients with CHF.

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“…Databases such as the Medical Information Mart for Intensive Care (MIMIC) have been used to build models to predict mortality (9,10) and morbidity (11,12). A predictive model may provide an early warning to clinicians before the manifestation of clinical signs.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a machine-learning model for prediction of hypoxemia after extubation in intensive care units

Xia¹,

Jin²,

Cao³

et al. 2022

Ann Transl Med

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Background: Extubation is the process of removing tracheal tubes so that patients maintain oxygenation while they start to breathe spontaneously. However, hypoxemia after extubation is an important issue for critical care doctors and is associated with patients' oxygenation, circulation, recovery, and incidence of postoperative complications. Accuracy and specificity of most related conventional models remain unsatisfactory. We conducted a predictive analysis based on a supervised machine-learning algorithm for the precise prediction of hypoxemia after extubation in intensive care units (ICUs).Methods: Data were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database for patients over age 18 who underwent mechanical ventilation in the ICU. The primary outcome was hypoxemia after extubation, and it was defined as a partial pressure of oxygen <60 mmHg after extubation. Variables and individuals with missing values greater than 20% were excluded, and the remaining missing values were filled in using multiple imputation. The dataset was split into a training set (80%) and final test set (20%). All related clinical and laboratory variables were extracted, and logistics stepwise regression was performed to screen out the key features. Six different advanced machine-learning models, including logistics regression (LOG), random forest (RF), K-nearest neighbors (KNN), support-vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were introduced for modelling. The best performance model in the first cross-validated dataset was further fine-tuned, and the final performance was assessed using the final test set.Results: A total of 14,777 patients were included in the study, and 1,864 of the patients' experienced hypoxemia after extubation. After training, the RF and LightGBM models were the strongest initial performers, and the area under the curve (AUC) using RF was 0.780 [95% confidence interval (CI), 0.755-0.805] and using LightGBM was 0.779 (95% CI, 0.752-0.806). The final AUC using RF was 0.792 (95% CI, 0.771-0.814) and using LightGBM was 0.792 (95% CI, 0.770-0.815).Conclusions: Our machine learning models have considerable potential for predicting hypoxemia after extubation, which help to reduce ICU morbidity and mortality.

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Predicting Duration of Mechanical Ventilation in Acute Respiratory Distress Syndrome Using Supervised Machine Learning

Cited by 24 publications

References 32 publications

Risk Factors and Mortality in Elderly ARDS COVID-19 Compared to Patients without COVID-19

Risk Factors and Mortality in Elderly ARDS COVID-19 Compared to Patients without COVID-19

A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Congestive Heart Failure

Development and validation of a machine-learning model for prediction of hypoxemia after extubation in intensive care units

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