Machine Learning Algorithms Predict Successful Weaning From Mechanical Ventilation Before Intubation: Retrospective Analysis From the Medical Information Mart for Intensive Care IV Database

Abstract

Background:The prediction of successful weaning from mechanical ventilation (MV) in advance of intubation can facilitate discussions regarding end-of-life care before unnecessary intubation.

Objective:We aimed to develop a machine learning–based model that predicts successful weaning from ventilator support based on routine clinical and laboratory data taken before or immediately after intubation.

Methods:We used the Medical Information Mart for Intensive Care IV database, which is an open-access database covering 524,740 admissions of 382,278 patients in Beth Israel Deaconess Medical Center, United States, from 2008 to 2019. We selected adult patients who underwent MV in the intensive care unit (ICU). Clinical and laboratory variables that are considered relevant to the prognosis of the patient in the ICU were selected. Data collected before or within 24 hours of intubation were used to develop machine learning models that predict the probability of successful weaning within 14 days of ventilator support. Developed models were integrated into an ensemble model. Performance metrics were calculated by 5-fold cross-validation for each model, and a permutation feature importance and Shapley additive explanations analysis was conducted to better understand the impacts of individual variables on outcome prediction.

Results:Of the 23,242 patients, 19,025 (81.9%) patients were successfully weaned from MV within 14 days. Using the preselected 46 clinical and laboratory variables, the area under the receiver operating characteristic curve of CatBoost classifier, random forest classifier, and regularized logistic regression classifier models were 0.860 (95% CI 0.852-0.868), 0.855 (95% CI 0.848-0.863), and 0.823 (95% CI 0.813-0.832), respectively. Using the ensemble voting classifier using the 3 models above, the final model revealed the area under the receiver operating characteristic curve of 0.861 (95% CI 0.853-0.869), which was significantly better than that of Simplified Acute Physiology Score II (0.749, 95% CI 0.742-0.756) and Sequential Organ Failure Assessment (0.588, 95% CI 0.566-0.609). The top features included lactate and anion gap. The model’s performance achieved a plateau with approximately the top 21 variables.

Conclusions:We developed machine learning algorithms that can predict successful weaning from MV in advance to intubation in the ICU. Our models can aid the appropriate management for patients who hesitate to decide on ventilator support or meaningless end-of-life care.

Introduction

Acute respiratory failure can be caused by various conditions, including pulmonary disease, cardiovascular disease, and neuromuscular disorder, or required respiratory support after major surgery [1]. Although invasive mechanical ventilation (MV) is a life-maintaining intervention used to assist or replace spontaneous respiration in patients with acute respiratory failure, the procedure is associated with a risk of severe complications such as ventilator-associated pneumonia, pulmonary edema, and acute respiratory distress syndrome [2].

Inevitably, a proportion of patients will be unable to recover rapidly from ventilator support, mandating the use of MV for an extended period. The duration of MV is linearly associated with poor outcomes, and the number of days of ventilation support directly correlates with daily incremental costs and unexpected medical conditions such as thromboembolic events and posttraumatic stress disorder [3]. Prolonged MV unavoidably accompanies tracheostomy and life-maintaining care, which is not usually desired by patients [4,5]. Such a decision is usually made within 14 days of MV [6]. Tracheostomy has its advantages in a lower frequency of laryngeal ulcers, less airway resistance, and ease of management [4,5]. However, prolonged MV and consequent tracheostomy are unlikely to benefit chronically ill patients with an expected dismal prognosis. It should only be performed if it aligns with the patient’s goals and preferences. The possibility of undergoing a tracheostomy can be a reason for hesitancy to intubate older or chronically ill patients.

For these reasons, successful early predictions of whether a patient will undergo prolonged MV can support clinical decision-making in many clinical aspects. Several risk factors, including underlying comorbidities, the site of intubation, laboratory or blood gas results, functional parameters, and critical care scoring systems, have been identified for successful weaning from MV [7]. However, as the predictive power using a single or a few variables was insufficient, there have been efforts to create a predictive model by assigning weights to each relevant variable.

Machine learning could potentially be a breakthrough in this type of prediction, where various factors are involved in a complex manner. Several studies have used machine learning techniques to predict successful extubation using a combination of multiple variables [8]. However, the models proposed to predict successful weaning from MV did not reflect various clinical situations requiring prediction before initiating MV, because most models collected variables and predicted outcomes at the time of MV progress, not before intubation. Other previous models have been suggested to anticipate prolonged MV or tracheostomy [9]. However, they were either short-term predictive models or unrealistic models that only predicted whether patients would receive prolonged MV but excluded patients who died before MV day 14. Given this background, we aimed to develop a thorough machine learning model that can predict the possibility of successful weaning from MV within 14 days after intubation, before undergoing intubation.

Methods

Data Source

Data on patients requiring MV were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 1.0 database. MIMIC-IV is a well-known, large-scale, single-center (Beth Israel Deaconess Medical Center), and open-access database covering 524,740 admissions of 382,278 patients to the center from 2008 to 2019 [10]. The relevant records include demographic data; International Classification of Diseases, Ninth Revision, Clinical Modification codes; hourly vital signs and input or output; laboratory tests and microbiological culture results; imaging data, treatment procedures; medication administration; and survival data. The database also provides multiple severity-of-illness scores generated from physiologic and laboratory variables on the first day of each intensive care unit (ICU) admission. MIMIC-IV has several advantages over its previous version, MIMIC-III. The composing data are relatively homogenous, because MIMIC-IV contains data entirely sourced from the clinical information system MetaVision (iMDSoft); the information of “procedure events,” one of the primary sources of clinical information in ICU, is entirely present; and a substantial number of patients is included.

Selection of Participants

For meticulous patient selection, patients with “Intubation” and “Invasive ventilation” codes appearing at least once in the “procedure event” or “chart event” files were selected. Additionally, patients with “Ventilator type” and “Ventilator mode” codes appearing 5 times or more within 24 hours after the first code were also included (Figure S1 in Multimedia Appendix 1). The exclusion criteria were as follows: (1) aged < 18 or > 100 years, (2) previous tracheostomy, and (3) missing Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPS II).

Data Collection and Outcome Definition

We collected clinical and laboratory variables recorded before and closest to the initiation of MV. For patients who did not have the values before intubation, the nearest value was obtained within 24 hours of intubation. To minimize the impact of intubation on each variable, we selected variables that are less likely to change dramatically after intubation. Clinical and laboratory variables considered relevant to patient prognosis in the ICU were selected by 2 clinicians (JK and HJK) from the list of variables included in the MIMIC-IV database. Any discrepancy was resolved by group discussion. They were as follows: age, sex, height, weight, Glasgow Coma Scale (eye), Glasgow Coma Scale (motor), hemoglobin, pH, lactate, albumin, anion gap, total bilirubin, bicarbonate, blood urea nitrogen, creatinine, platelet count, prothrombin time, neutrophil or lymphocyte ratio, sodium, potassium, white blood cell count, body temperature, type of admission (medical, scheduled surgical, and unscheduled surgical), type of insurance (public and other), primary language (English and other), marital status (couple and single), race and ethnicity (Asian, Black, Hispanic, White, and others), type of ICU (medical, surgery, and others), and underlying comorbidities (myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, mild liver disease, diabetes without complication, diabetes with complication, paraplegia, renal disease, malignancy, severe liver disease, metastatic solid tumor, and acquired immune deficiency syndrome).

The missing values were imputed using multiple imputations by chained equations for continuous variables and the k-nearest neighbor method for categorical variables. The missing rates are depicted in Figure S2 in Multimedia Appendix 1. As a comparator, 2 severity-of-illness scores, that is, the SOFA and SAPS II scores, were calculated using the codes from Google’s BigQuery database [11]. The primary outcome was successful weaning within 14 days of intubation, defined as documented MV discontinuation without death.

Model Development

The development and validation of our model were performed according to the “Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research” [12]. The checklist is available in Table S1 in Multimedia Appendix 1. Several machine learning algorithms were used to develop predictive models, such as regularized logistic regression classifier (RLRC) [13], random forest classifier (RFC) [14], CatBoost classifier (CBC) [15], and voting classifier (VC) ensembles [16]. Since the data set was imbalanced, we used an algorithm-level approach to handle the imbalance. Specifically, we used Cohen κ maximizing threshold of the threshold-moving approach, cost-sensitive learning, and ensemble method of VC to reduce bias or variance and improve the stability of machine learning algorithms [17-19].

We used the 5-fold cross-validation method with a fixed random seed to achieve reproducibility. The 5-fold cross-validation divides the data into 5 equal partitions, training the model on 4 and testing it on the remaining 1. This process is repeated 5 times, with each partition used once as the test set. The performance metric is the average performance across all 5 iterations. This method is a way to demonstrate a model’s robustness in the absence of external data and avoids the risk of model overfitting. Mean with 95% CIs of the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), Cohen κ, and F₁-score of the models were calculated using the 5-fold cross-validation. The AUPRC and AUROC were used to verify the effectiveness of our proposed model and compare it with other models. AUROC can be used as a diagnostic test to discriminate between actual positives and negatives. However, AUPRC is used as an alternative to AUROC for tasks with highly skewed class distribution [20,21]. Although AUROC can evaluate classifiers when there is a class imbalance, it can present an overly optimistic view of performance if there is a large skew in the class distribution [22]. Thus, we showed both AUROC and AUPRC to prove the superiority of our proposed model to other models.

Cohen κ is a metric for evaluating the classification algorithm’s consistency based on its predictions [22]. F₁-score is also used as an evaluation and comparison metric. F₁-score is the measure of the weighted average of 2 evaluation metrics of precision and sensitivity. Therefore, we chose the F₁-score to obtain harmonic means between precision and sensitivity. Likewise, the performance parameters for the SOFA and SAPS II scores were calculated, and the AUROCs of each model were compared using the DeLong test [23].

The VC model’s confusion matrix was presented using Cohen κ maximizing threshold value. To determine the model’s threshold, we performed a manual visual inspection with a graph of the threshold versus Cohen κ [17]. After exploring the threshold at which Cohen κ value can be maximized, we presented this result in the confusion matrix by adjusting true positives, true negatives, false positives, and false negatives using the threshold at which this value can be maximized.

To determine the optimal hyperparameter setting, the GridSearchCV library (version 0.22) was used to search multiple optimal parameter values to fit estimators automatically. We drew calibration plots for each algorithm (Figure S3 in Multimedia Appendix 1) [24,25]. Finally, to better understand how individual variables impact the outcome prediction, a permutation feature importance and Shapley additive explanations (SHAP) analysis on the best-performing model was conducted [14,26]. For model development and validation, Python (version 3.6.9; Python Software Foundation) and its packages such as NumPy (version 1.19.5) [27], pandas (version 1.1.5) [28], scikit-learn (version 0.23.2) [29], Matplotlib (version 3.3.4) [30], seaborn (version 0.11.2) [31], rpy2 (version 3.4.5) [32], SciPy (version 1.5.4; Enthought) [33], and SHAP (version 0.41.0) [26,34], as well as R (version 3.4.4; R Core Team) [35] and its package pROC (version 1.18.0) [23], were used [36]. The codes used in this study are made available at GitHub [37] for noncommercial use.

Other Statistical Considerations

To compare the baseline characteristics, categorical variables were presented as total numbers (percentages) and compared using Fisher exact test. Continuous variables were presented as means (SD) and compared using the Wilcoxon rank-sum test. All statistical analyses in this study were performed using Google BigQuery, Python (version 3.6.9), and R (version 3.4.4), and P < .05 were considered statistically significant.

Ethical Considerations

The research resource, which includes the collection of patient information, was assessed by the Institutional Review Board at Beth Israel Deaconess Medical Center. They provided a waiver of informed consent and endorsed the data-sharing initiative. Furthermore, the study received approval from the Institutional Review Board of Seoul National University Bundang Hospital (B-2201-733-002).

Results

Baseline Characteristics

Out of the 24,379 patients screened, 23,242 were included in the analysis, and 19,025 (81.9%) successfully weaned from MV within 14 days (Figure 1). For those who successfully weaned from MV, the duration of MV (mean 1.7, SD 2.7 vs mean 8.1, SD 10.6 d; P < .001), age (mean 63.7, SD 15.9 vs mean 67.8, SD 15.8 y; P < .001), blood urea nitrogen (mean 22.4, SD 17.2 vs mean 33.1, SD 25.4 mg/dL; P < .001), creatinine (mean 1.2, SD 1.2 vs mean 1.7, SD 1.5 mg/dL; P < .001), anion gap (mean 13.7, SD 4.0 vs mean 17.3, SD 5.8 mEq/L; P < .001), SAPS II (mean 37.5, SD 13.3 vs mean 51.6, SD 16.7; P < .001), and SOFA score (mean 2.7, SD 2.5 vs mean 3.9, SD 3.5; P < .001) were lower, whereas pH (mean 7.4, SD 0.1 vs mean 7.3, SD 0.1; P < .001) and bicarbonate level (mean 23.4, SD 4.2 vs mean 21.1, SD 5.9 mEq/L; P < .001) were higher than the other group of patients (Table 1). The proportions of patients with public insurance (56.5% vs 48.7%; P < .001), single marital status (60.1% vs 51.2%; P < .001), and admission to a medical care unit (36.3% vs 19.1%; P < .001) were lower for those who had been weaned from MV within 14 days. Underlying comorbidities were also less common in these patients (Table S2 in Multimedia Appendix 1).

Figure 1. Flowchart of the patient selection process. Patients with evidence of endotracheal intubation were identified in the Medical Information Mart for Intensive Care IV database. After careful selection, patients were divided into 2 groups according to whether they had been successfully weaned from MV within 14 days of intubation or not. MV: mechanical ventilation; SAPS II: Simplified Acute Physiology Score II; SOFA: Sequential Organ Failure Assessment.

Table 1.Baseline characteristics of patients in the intensive care unit according to successful weaning from mechanical ventilation within 14 days.

Variables	All patients (N=23,242)	Successful weaning (n=19,025)	Prolonged mechanical ventilation or mortality (n=4217)	P value
Age (y), mean (SD)	64.4 (15.9)	63.7 (15.9)	67.8 (15.8)	< .001
Male sex, n (%)	14,274 (61.4)	11,935 (62.7)	2339 (55.5)	< .001
Height (cm), mean (SD)	169.7 (10.8)	170 (10.8)	168.7 (10.9)	< .001
Weight (kg), mean (SD)	83.3 (23.2)	83.8 (22.7)	81.0 (25.4)	< .001
Body temperature (°C), mean (SD)	36.6 (1.3)	36.7 (1.2)	36.5 (1.6)	< .001
WBCa count (per 109/L), mean (SD)	13.0 (7.5)	12.7 (6.9)	14.3 (9.8)	< .001
NLb ratio, mean (SD)	9.1 (10.9)	8.3 (9.3)	12.3 (15.7)	< .001
Hemoglobin (g/dL), mean (SD)	10.4 (2.1)	10.4 (2.1)	10.6 (2.4)	< .001
Platelet count (per 109/L), mean (SD)	190.4 (100.9)	188.9 (97.1)	197.4 (116.2)	< .001
BUNc (mg/dL), mean (SD)	24.4 (19.4)	22.4 (17.2)	33.1 (25.4)	< .001
Creatinine (mg/dL), mean (SD)	1.3 (1.3)	1.2 (1.2)	1.7 (1.5)	< .001
Albumin (g/dL), mean (SD)	3.2 (0.4)	3.2 (0.4)	3.1 (0.5)	< .001
Total bilirubin (mg/dL), mean (SD)	1.3 (3.1)	1.1 (2.4)	2.2 (5.2)	< .001
Prothrombin time (INRd), mean (SD)	1.5 (0.7)	1.4 (0.6)	1.7 (1.1)	< .001
pH, mean (SD)	7.3 (0.1)	7.4 (0.1)	7.3 (0.1)	< .001
Sodium (mEq/L), mean (SD)	138.9 (4.6)	138.9 (4.3)	138.9 (5.9)	.900
Potassium (mEq/L), mean (SD)	4.2 (0.7)	4.2 (0.7)	4.3 (0.8)	< .001
Lactate (mmol/L), mean (SD)	2.6 1.9)	2.4 (1.5)	3.7 (3.1)	< .001
Bicarbonate (mEq/L), mean (SD)	23.0 (4.7)	23.4 (4.2)	21.1 (5.9)	< .001
Anion gap (mEq/L), mean (SD)	14.4 (4.6)	13.7 (4.0)	17.3 (5.8)	< .001
SOFAe, mean (SD)	2.9 (2.8)	2.7 (2.5)	3.9 (3.5)	< .001
SAPS IIf, mean (SD)	40.0 (15.0)	37.5 (13.3)	51.6 (16.7)	< .001
Duration of MVg (days), mean (SD)	2.8 (5.7)	1.7 (2.7)	8.1 (10.6)	< .001

^aWBC: white blood cell.

^bNL: neutrophil or lymphocyte.

^cBUN: blood urea nitrogen.

^dINR: international normalized ratio.

^eSOFA: Sequential Organ Failure Assessment.

^fSAPS II: Simplified Acute Physiology Score II.

^gMV: mechanical ventilator.

Development of the Prediction Models

The AUROC and AUPRC values resulting from 5-fold cross-validation are shown in Figure 2. The AUROC values (Figure 2A) of the VC, CBC, RFC, and RLRC models for the prediction of successful weaning were 0.861 (95% CI 0.853-0.869), 0.860 (95% CI 0.852-0.868), 0.855 (95% CI 0.848-0.863), and 0.823 (95% CI 0.813-0.832), respectively. The 2 conventional scoring models showed AUROC values of 0.749 (95% CI 0.742-0.756) for SAPS II and 0.588 (95% CI 0.566-0.609) for the SOFA score. Figure 2B presents the positive predictive value against sensitivity, with AUPRC values of 0.589 (95% CI 0.564-0.614), 0.590 (95% CI 0.570-0.610), 0.577 (95% CI 0.551-0.603), and 0.497 (95% CI 0.470-0.525), respectively, in each machine learning model. Detailed descriptions of various performance metrics are presented in Table 2. We also generated a confusion matrix for the cross-validation of the VC model (Figure S4 in Multimedia Appendix 1).

Figure 2. (A) Receiver operating characteristic curves and (B) precision-recall curves of the developed machine learning models to predict successful weaning from mechanical ventilation within 14 days of intubation. The area under receiver operating characteristics curves (AUROCs) and area under precision-recall curves (AUPRCs) and their 95% CIs for all 6 models are shown in the legends. For comparability, the random classifier is indicated by a black dashed-dotted line. CBC: CatBoost classifier; RFC: random forest classifier; RLRC: regularized logistic regression classifier; SAPS II: Simplified Acute Physiology Score II; SOFA: Sequential Organ Failure Assessment; VC: voting classifier.

Table 2.Performance metrics of the developed machine learning models, along with SOFA^a score and SAPS II^b,^c.

Model	AUROCd, mean (95% CI)	AUPRCe, mean (95% CI)	Cohen κ, mean (95% CI)	F1-score, mean (95% CI)
VCf	0.861 (0.853-0.869)g,h	0.589 (0.564-0.614)	0.413 (0.404-0.421)	0.554 (0.550-0.558)
CBCi	0.860 (0.852-0.868)g,h	0.590 (0.570-0.610)	0.400 (0.383-0.417)	0.546 (0.536-0.557)
RFCj	0.855 (0.848-0.863)g,h	0.577 (0.551-0.603)	0.392 (0.380-0.404)	0.540 (0.534-0.547)
RLRCk	0.823 (0.813-0.832)g,h	0.497 (0.570-0.525)	0.359 (0.348-0.369)	0.515 (0.509-0.521)
SAPS II	0.749 (0.742-0.756)g	0.438 (0.414-0.462)	0.280 (0.263-0.297)	0.451 (0.440-0.462)
SOFA	0.588 (0.566-0.609)h	0.284 (0.264-0.304)	0.121 (0.096-0.147)	0.330 (0.311-0.349)

^aSOFA: Sequential Organ Failure Assessment.

^bSAPS II: Simplified Acute Physiology Score II.

^cValues were calculated from 5-fold cross-validation. Hypothesis tests were conducted to determine whether the AUROC values of the models using machine learning algorithms were equal to those of conventional scores.

^dAUROC: area under the receiver operating characteristics curve.

^eAUPRC: area under the precision-recall curve.

^fVC: voting classifier.

^gP < .001 compared to SOFA score.

^hP < .001 compared to SAPS II.

ⁱCBC: CatBoost classifier.

^jRFC: random forest classifier.

^kRLRC: regularized logistic regression classifier.

Feature Importance of Each Variable

We chose the VC model as our representative model. The included variables in the model were ranked according to their information gain, and the top 3 features were lactate concentration, anion gap, and body temperature (Figure 3). To better understand the direction of influence each feature has on this model, the SHAP algorithm was implemented for this model to explain for each feature the magnitude and direction of its impact on the outcome prediction. The top risk features included anion gap, age, presence of cerebrovascular disease, and blood urea nitrogen concentration. Specifically, a higher value or the presence of a variable indicates a higher chance of failure to wean from MV within 14 days (Figure 4).

Figure 3. Important explanatory variables for the ensemble voting classifier model. The means and SD of the top 21 important explanatory variables for the voting classifier model using 5-fold cross-validation and test set permutation are indicated by bars and error bars, respectively. BUN: blood urea nitrogen; GCS: Glasgow Coma Scale; ICU: intensive care unit; NL: neutrophil-to-lymphocyte; WBC: white blood cell.

Figure 4. The Beeswarm summary plot in the SHAP package with a randomly selected test set among a 5-fold cross-validation. This figure indicates which explanatory variables have the most significant influence on the model’s predicted value and the tendency of the model’s outcome probability as a function of the original value of an explanatory variable simultaneously. Each colored dot represents one variable value for a patient, for which larger values are presented in red and smaller values in blue. The SHAP value, a simplified and computer-friendly Shapley value, is shown on the horizontal axis and is a surrogate of the contribution of a variable value to the model output. BUN: blood urea nitrogen; GCS: Glasgow Coma Scale; SHAP: Shapley additive explanations.

Change of Model Performance With Variables

We assessed the performance metrics (Cohen κ, AUROC, F₁-score, and balanced accuracy) of the VC model according to the number of features included (Figure 5). Each metric’s performance was calculated as the variables with the highest feature importance were sequentially added. The model reached its plateau performance in all 4 metrics with approximately 21 variables.

Figure 5. Performance metrics of the ensemble voting classifier model according to the addition of explanatory variables. (A) Cohen κ, (B) AUROC, (C) F1-score, and (D) balanced accuracy of the ensemble voting classifier are shown as the number of explanatory variables in the training sets in descending order of feature importance coefficients. Mean (dot) and SD (error bar) were computed from the data sets by 5-fold cross-validation. The model reached its plateau performance in all 4 metrics with approximately 21 variables. AUROC: area under the receiver operating characteristics curve; RFI: relative feature importance.

Discussion

Principal Findings and Clinical Implications

Within the context of the ICU, physicians often rely on clinical presentations to gauge the likelihood of a patient's successful weaning from MV prior to endotracheal intubation. Translating these clinical intuitions into quantifiable metrics, however, presents a challenge. To address this, our study meticulously developed and validated machine learning–based models designed to predict successful weaning from MV either before or immediately postintubation in critically ill patients on ventilator support. Notably, our model's predictive accuracy surpassed that of traditional prognostic scoring systems commonly used for ICU patients.

The practical use of our model is underscored by its ability to harness readily available data from electronic health records, including vital signs, foundational laboratory results, and patient medical histories. This facilitates the identification of patients at an elevated risk of extended MV reliance. The advantages of using our model are multifaceted. Prompt identification of high-risk patients allows for the timely initiation of therapeutic interventions, potentially curtailing the duration of MV. Such early identification ensures a judicious allocation of resources, optimizing the use of specialized equipment and expert personnel, leading to enhanced patient outcomes and cost-effectiveness. Additionally, it paves the way for proactive discharge planning, bolstering patient satisfaction, and alleviating pressures on the health care infrastructure.

Comparison With Prior Work

This is the first study to develop a prognostic model that predicts relatively long-term outcomes (14 days) based on variables within a day of intubation. The model characteristics render it clinically pragmatic and facilitate improved discussions about end-of-life care or prolonged MV with a tracheostomy. Previous efforts to predict the prognosis of patients undergoing MV in ICUs have shown several drawbacks. Clark et al [9,38] suggested a model consisting of clinical variables (intubation in the ICU, tachycardia, renal dysfunction, acidemia, elevated creatinine, and decreased HCO₃^– concentration) to identify individuals who may need prolonged MV at the time of intubation. However, their model was derived from and validated in relatively small patient populations (99 and 225 patients, respectively), and the AUROC value for prolonged MV prediction was about 0.75. Moreover, patients who died within 2 weeks of starting MV were excluded from the analysis, leading to a selection bias. Several other studies exist but only predicted mortality [39] and short-term outcomes [40], or they did not consider death in model building [41].

Numerous clinical factors have been proposed in predictive models for patients with MV. The I-TRACH (Intubation in the ICU, Tachycardia, Renal Dysfunction, Acidemia, Creatinine, and Decreased HCO₃) model previously extracted tachycardia, renal dysfunction, acidemia, and a decreased HCO₃^– concentration as the main variables for constructing a scoring system [38]. In another study that reported the prediction of 30-day mortality, the essential features in the models were Acute Physiology and Chronic Health Evaluation II score, Charlson Comorbidity Index, use of norepinephrine, and base excess [39]. In our study, lactate level and anion gap were the 2 most important predictors in the final VC model. Per the findings of this study, several prior reports have emphasized the prognostic importance of lactate level and anion gap. The efficacy of early lactate-guided therapy in ICU patients has been reported [42]. The anion gap, a surrogate for levels of unmeasured anions, has been reported in a meta-analysis as an indicator of mortality in critically ill patients [43].

Strengths

We proposed the VC ensemble comprising RLRC, RFC, and CBC as our representative model. Historically, logistic and linear regression models have been used for prognosis tasks in clinical decisions concerning ICU-admitted patients [44]. Still, they are not fit for predictor variables with skewed distributions and tend to overfit. To avoid these shortcomings, more complex modeling approaches have been proposed [45]. First, RLRC does not rely on multivariate normality and equal within-group covariance matrices, but predictions require large-scale sample data for stable outcomes [13,46]. Second, RFC works well on data with several input variables and improves its classification accuracy because it keeps bias low and reduces variance. Still, the interpretation is complex, and evaluation is slow [14]. Third, CBC requires lower computational costs but shows better accuracy than other tree-based models and support vector machines [15,47]. The 3 models (RLRC, RFC, and CBC) showed a tradeoff between precision and recall [48]; therefore, the use of the VC ensemble method improves performance by reducing the variance component of prediction errors made by the contributing models [16].

Apart from the thorough development of the ensemble model, our study has its strength due to the relatively high number of included patients (n=23,242) provided by the MIMIC-IV database, which is a well-established open database derived from an ICU in the United States. This is the first study to establish a machine learning model to predict the weaning probability based on MIMIC-IV data. Moreover, precise search terms such as “intubation/invasive ventilation” and “ventilator type/mode” were used to establish a more stringent patient selection and outcome definition. Such criteria can provide an example for the selection processes of intubated patients for future studies using the MIMIC-IV database.

Limitations

Despite our meaningful findings, there are some inherent limitations to our study. First, some imbalance in patient numbers was noted between those with and without successful weaning from MV (81.9% vs 18.1%, respectively). Therefore, we used an algorithm-level approach to handle the imbalance and presented various performance metrics such as AUPRC and Cohen κ. Second, some variables had a considerable proportion of missing data or were collected after intubation. Although small, 915 patients with missing SOFA and SAPS II scores were also excluded from our analysis, leading to concerns of selection bias (Table S3 in Multimedia Appendix 1). However, the presence of missing data reflects our real-world medical practice, and we used variables not likely to change dramatically according to intubation (eg, body temperature, blood urea nitrogen, and underlying diseases). Third, our model lacks external validity due to its innate nature as a single-center study. However, our model is grounded on universally recognized variables, including vital signs, underlying comorbidities, and laboratory findings. The open nature of our data set, combined with the web-based availability of our code, simplifies validation efforts for other researchers. Future studies for external validation can strengthen our models.

Conclusions

In conclusion, we developed and validated a VC ensemble machine learning model that can effectively predict successful weaning from MV within 14 days before or immediately after intubation. Our study indicates that machine learning algorithms may facilitate clinical decision-making, such as identifying patients more likely to benefit from MV before or immediately after endotracheal intubation. This information can relieve the burden and aid doctors in suggesting appropriate management for patients at risk of endotracheal intubation in the ICU, notably for those who hesitate to decide on ventilator support or meaningless end-of-life care due to advanced age or the presence of several comorbidities.

Acknowledgments

This work was supported by grant 13-2022-0015 from the Seoul National University Bundang Hospital Research Fund and was also conducted with the support of Korea Human Resource Development Institute for Health and Welfare’s medical artificial intelligence expert training course (HI18C2386) with the funding of the Ministry of Health and Welfare (Korea Health Industry Development Institute). The sponsor did not have any role in the study. The codes used in this study are made available at GitHub [37] for noncommercial use.

Authors' Contributions

JK, YKK, HY, and HJK conceived and planned the study and wrote the paper. YKK, HK, HJ, SK, YK, DY, and HY analyzed the data. All authors contributed to the interpretation of the results. All authors provided critical feedback and helped shape the research, analysis, and paper.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Supplementary tables and figures on guidelines and additional data not presented in the main text.

PDF File (Adobe PDF File), 596 KB

------------

References

Esteban A, Anzueto A, Frutos F, Alía I, Brochard L, Stewart TE, et al. Characteristics and outcomes in adult patients receiving mechanical ventilation: a 28-day international study. JAMA 2002;287(3):345-355 [https://jamanetwork.com/journals/jama/fullarticle/194560] Slutsky AS, Ranieri VM. Ventilator-induced lung injury. N Engl J Med 2013;369(22):2126-2136 Chatila WM, Criner GJ. Complications of long-term mechanical ventilation. Respir Care Clin N Am 2002;8(4):631-647 Hsu CL, Chen KY, Chang CH, Jerng JS, Yu CJ, Yang PC. Timing of tracheostomy as a determinant of weaning success in critically ill patients: a retrospective study. Crit Care 2005;9(1):R46-R52 [https://ccforum.biomedcentral.com/articles/10.1186/cc3018] Mallick A, Bodenham AR. Tracheostomy in critically ill patients. Eur J Anaesthesiol 2010;27(8):676-682 [https://journals.lww.com/ejanaesthesiology/fulltext/2010/08000/tracheostomy_in_critically_ill_patients.2.aspx] Siempos II, Ntaidou TK, Filippidis FT, Choi AMK. Effect of early versus late or no tracheostomy on mortality and pneumonia of critically ill patients receiving mechanical ventilation: a systematic review and meta-analysis. Lancet Respir Med 2015;3(2):150-158 Trudzinski FC, Neetz B, Bornitz F, Müller M, Weis A, Kronsteiner D, et al. Risk factors for prolonged mechanical ventilation and weaning failure: a systematic review. Respiration 2022;101(10):959-969 [https://karger.com/res/article/101/10/959/829111/Risk-Factors-for-Prolonged-Mechanical-Ventilation] 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 [https://europepmc.org/abstract/MED/36035403] Clark PA, Lettieri CJ. Clinical model for predicting prolonged mechanical ventilation. J Crit Care 2013;28(5):880.e1-880.e7 Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, et al. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res 2021;8(1):44 [https://mmrjournal.biomedcentral.com/articles/10.1186/s40779-021-00338-z] MIT-LCP / mimic-code. GitHub. URL: https://github.com/MIT-LCP/mimic-code/tree/main/mimic-iv/concepts/score [accessed 2022-11-03] Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 2016;18(12):e323 [https://www.jmir.org/2016/12/e323/] Salehi F, Abbasi E, Hassibi B. The impact of regularization on high-dimensional logistic regression. 2019 Presented at: Proceedings of the 33rd International Conference on Neural Information Processing Systems; December 8-14, 2019; Vancouver, Canada p. 12005-12015 Breiman L. Random forests. Mach Learn 2001;45(1):5-32 Prokhorenkova L, Gusev G, Vorobev A, Dorogush A, Gulin A. CatBoost: unbiased boosting with categorical features. Red Hook, NY, US: Curran Associates Inc; 2018 Presented at: Proceedings of the 32nd International Conference on Neural Information Processing Systems; December 3-8, 2018; Montréal, Canada p. 6639-6649 Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res 1999;11(1):169-198 [https://jair.org/index.php/jair/article/view/10239/24370] Chang CH, Yang JT, Lee MH. A novel "Maximizing Kappa" approach for assessing the ability of a diagnostic marker and its optimal cutoff value. J Biopharm Stat 2015;25(5):1005-1019 Li F, Zhang X, Zhang X, Du C, Xu Y, Tian YC. Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets. Inf Sci 2018;422:242-256 Naseem U, Rashid J, Ali L, Kim J, Haq QEU, Awan MJ, et al. An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers. IEEE Access 2022;10:78242-78252 [https://ieeexplore.ieee.org/document/9773160/authors#authors] Provost FJ, Fawcett T, Kohavi R. The case against accuracy estimation for comparing induction algorithms. 1998 Presented at: Proceedings of the Fifteenth International Conference on Machine Learning; July 24-27, 1998; US p. 445-453 URL: http://ai.stanford.edu/people/ronnyk/roc.pdf Drummond C, Holte RC. Explicitly representing expected cost: an alternative to ROC representation. 2000 Presented at: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 20-23, 2000; Boston, MA, USA p. 198-207 Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. 2006 Presented at: Proceedings of the 23rd International Conference on Machine Learning; June 25-29, 2006; Pittsburgh, PA, USA p. 233-240 Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77 [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-77] Campagner A, Famiglini L, Cabitza F. Re-calibrating machine learning models using confidence interval bounds. 2022 Presented at: 19th International Conference on Modeling Decisions for Artificial Intelligence; August 30-September 2, 2022; Sant Cugat, Spain p. 132-142 Zadrozny B, Elkan C. Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. 2001 Presented at: Proceedings of the Eighteenth International Conference on Machine Learning; June 28-July 1, 2001; US p. 609-616 Baptista ML, Goebel K, Henriques EMP. Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artif Intell 2022;306:103667 [https://www.sciencedirect.com/science/article/pii/S0004370222000078?via%3Dihub] Oliphant TE. A Guide to NumPy. USA: Trelgol Publishing; 2006. McKinney W. pandas: a foundational Python library for data analysis and statistics. Python High Perform Sci Comput 2011;14(9):1-9 [https://www.dlr.de/sc/portaldata/15/resources/dokumente/pyhpc2011/submissions/pyhpc2011_submission_9.pdf] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12(85):2825-2830 [https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf] Hunter JD. Matplotlib: A 2D graphics environment. Comput Sci Eng 2007;9(3):90-95 Waskom ML. seaborn: statistical data visualization. J Open Source Softw 2021;6(60):3021 [https://joss.theoj.org/papers/10.21105/joss.03021] rpy2 / rpy2. GitHub. URL: https://github.com/rpy2/rpy2 [accessed 2022-09-16] Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020;17(3):261-272 [https://air.unimi.it/handle/2434/848184] Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Red Hook, NY, US: Curran Associates Inc; 2017 Presented at: Proceedings of the 31st International Conference on Neural Information Processing Systems; December 4-9, 2017; California, USA p. 4768-4777 R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2010. Van Rossum G, Drake FL. Python Tutorial. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica; 1995. GitHub. URL: https://github.com/yihahn/ml_predict_prolongedMV [accessed 2023-10-17] Clark PA, Inocencio RC, Lettieri CJ. I-TRACH: validating a tool for predicting prolonged mechanical ventilation. J Intensive Care Med 2018;33(10):567-573 Kim JH, Kwon YS, Baek MS. Machine learning models to predict 30-day mortality in mechanically ventilated patients. J Clin Med 2021;10(10):2172 [https://www.mdpi.com/resolver?pii=jcm10102172] Jia Y, Kaul C, Lawton T, Murray-Smith R, Habli I. Prediction of weaning from mechanical ventilation using convolutional neural networks. Artif Intell Med 2021;117:102087 Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res 2018;228:179-187 Jansen TC, van Bommel J, Schoonderbeek FJ, Sleeswijk Visser SJ, van der Klooster JM, Lima AP, et al. Early lactate-guided therapy in intensive care unit patients: a multicenter, open-label, randomized controlled trial. Am J Respir Crit Care Med 2010;182(6):752-761 [https://www.atsjournals.org/doi/10.1164/rccm.200912-1918OC] Glasmacher SA, Stones W. Anion gap as a prognostic tool for risk stratification in critically ill patients—a systematic review and meta-analysis. BMC Anesthesiol 2016;16(1):68 [https://bmcanesthesiol.biomedcentral.com/articles/10.1186/s12871-016-0241-y] Li F, Xin H, Zhang J, Fu M, Zhou J, Lian Z. Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database. BMJ Open 2021;11(7):e044779 [https://bmjopen.bmj.com/lookup/pmidlookup?view=long & pmid=34301649] Nori N, Kashima H, Yamashita K, Ikai H, Imanaka Y. Simultaneous modeling of multiple diseases for mortality prediction in acute hospital care. New York, NY, US: Association for Computing Machinery; 2015 Presented at: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 10-13, 2015; Sydney, NSW, Australia p. 855-864 Simpson GL, Birks HJB. Statistical learning in palaeolimnology. In: Birks HJB, Lotter AF, Juggins S, Smol JP, editors. Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques. Dordrecht, Netherlands: Springer; 2012:249-327 Mancini A, Vito L, Marcelli E, Piangerelli M, De Leone R, Pucciarelli S, et al. Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS". BMC Bioinformatics 2020;21(Suppl 10):347 [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03566-7] Erenel Z, Altınçay H. Improving the precision-recall trade-off in undersampling-based binary text categorization using unanimity rule. Neural Comput Appl 2013;22(S1):83-100

------------

Abbreviations

AUPRC: area under the precision-recall curve

AUROC: area under the receiver operating characteristic curve

CBC: CatBoost classifier

I-TRACH: Intubation in the Intensive Care Unit, Tachycardia, Renal Dysfunction, Acidemia, Creatinine, and Decreased HCO3

ICU: intensive care unit

MIMIV-IV: Medical Information Mart for Intensive Care IV

MV: mechanical ventilation

RFC: random forest classifier

RLRC: regularized logistic regression classifier

SAPS II: Simplified Acute Physiology Score II

SHAP: Shapley additive explanations

SOFA: Sequential Organ Failure Assessment

VC: voting classifier

------------

Edited by A Mavragani; submitted 02.12.22; peer-reviewed by J Wang, F He; comments to author 04.01.23; revised version received 23.02.23; accepted 08.10.23; published 14.11.23

References

Esteban A, Anzueto A, Frutos F, Alía I, Brochard L, Stewart TE, et al. Characteristics and outcomes in adult patients receiving mechanical ventilation: a 28-day international study. JAMA 2002;287(3):345-355 [https://jamanetwork.com/journals/jama/fullarticle/194560]

Slutsky AS, Ranieri VM. Ventilator-induced lung injury. N Engl J Med 2013;369(22):2126-2136

Chatila WM, Criner GJ. Complications of long-term mechanical ventilation. Respir Care Clin N Am 2002;8(4):631-647

Hsu CL, Chen KY, Chang CH, Jerng JS, Yu CJ, Yang PC. Timing of tracheostomy as a determinant of weaning success in critically ill patients: a retrospective study. Crit Care 2005;9(1):R46-R52 [https://ccforum.biomedcentral.com/articles/10.1186/cc3018]

Mallick A, Bodenham AR. Tracheostomy in critically ill patients. Eur J Anaesthesiol 2010;27(8):676-682 [https://journals.lww.com/ejanaesthesiology/fulltext/2010/08000/tracheostomy_in_critically_ill_patients.2.aspx]

Siempos II, Ntaidou TK, Filippidis FT, Choi AMK. Effect of early versus late or no tracheostomy on mortality and pneumonia of critically ill patients receiving mechanical ventilation: a systematic review and meta-analysis. Lancet Respir Med 2015;3(2):150-158

Trudzinski FC, Neetz B, Bornitz F, Müller M, Weis A, Kronsteiner D, et al. Risk factors for prolonged mechanical ventilation and weaning failure: a systematic review. Respiration 2022;101(10):959-969 [https://karger.com/res/article/101/10/959/829111/Risk-Factors-for-Prolonged-Mechanical-Ventilation]

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 [https://europepmc.org/abstract/MED/36035403]

Clark PA, Lettieri CJ. Clinical model for predicting prolonged mechanical ventilation. J Crit Care 2013;28(5):880.e1-880.e7

Wu WT, Li YJ, Feng AZ, Li L, Huang T, Xu AD, et al. Data mining in clinical big data: the frequently used databases, steps, and methodological models. Mil Med Res 2021;8(1):44 [https://mmrjournal.biomedcentral.com/articles/10.1186/s40779-021-00338-z]

MIT-LCP / mimic-code. GitHub. URL: https://github.com/MIT-LCP/mimic-code/tree/main/mimic-iv/concepts/score [accessed 2022-11-03]

Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 2016;18(12):e323 [https://www.jmir.org/2016/12/e323/]

Salehi F, Abbasi E, Hassibi B. The impact of regularization on high-dimensional logistic regression. 2019 Presented at: Proceedings of the 33rd International Conference on Neural Information Processing Systems; December 8-14, 2019; Vancouver, Canada p. 12005-12015

Breiman L. Random forests. Mach Learn 2001;45(1):5-32

Prokhorenkova L, Gusev G, Vorobev A, Dorogush A, Gulin A. CatBoost: unbiased boosting with categorical features. Red Hook, NY, US: Curran Associates Inc; 2018 Presented at: Proceedings of the 32nd International Conference on Neural Information Processing Systems; December 3-8, 2018; Montréal, Canada p. 6639-6649

Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res 1999;11(1):169-198 [https://jair.org/index.php/jair/article/view/10239/24370]

Chang CH, Yang JT, Lee MH. A novel "Maximizing Kappa" approach for assessing the ability of a diagnostic marker and its optimal cutoff value. J Biopharm Stat 2015;25(5):1005-1019

Li F, Zhang X, Zhang X, Du C, Xu Y, Tian YC. Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets. Inf Sci 2018;422:242-256

Naseem U, Rashid J, Ali L, Kim J, Haq QEU, Awan MJ, et al. An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers. IEEE Access 2022;10:78242-78252 [https://ieeexplore.ieee.org/document/9773160/authors#authors]

Provost FJ, Fawcett T, Kohavi R. The case against accuracy estimation for comparing induction algorithms. 1998 Presented at: Proceedings of the Fifteenth International Conference on Machine Learning; July 24-27, 1998; US p. 445-453 URL: http://ai.stanford.edu/people/ronnyk/roc.pdf

Drummond C, Holte RC. Explicitly representing expected cost: an alternative to ROC representation. 2000 Presented at: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 20-23, 2000; Boston, MA, USA p. 198-207

Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. 2006 Presented at: Proceedings of the 23rd International Conference on Machine Learning; June 25-29, 2006; Pittsburgh, PA, USA p. 233-240

Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77 [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-77]

Campagner A, Famiglini L, Cabitza F. Re-calibrating machine learning models using confidence interval bounds. 2022 Presented at: 19th International Conference on Modeling Decisions for Artificial Intelligence; August 30-September 2, 2022; Sant Cugat, Spain p. 132-142

Zadrozny B, Elkan C. Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. 2001 Presented at: Proceedings of the Eighteenth International Conference on Machine Learning; June 28-July 1, 2001; US p. 609-616

Baptista ML, Goebel K, Henriques EMP. Relation between prognostics predictor evaluation metrics and local interpretability SHAP values. Artif Intell 2022;306:103667 [https://www.sciencedirect.com/science/article/pii/S0004370222000078?via%3Dihub]

Oliphant TE. A Guide to NumPy. USA: Trelgol Publishing; 2006.

McKinney W. pandas: a foundational Python library for data analysis and statistics. Python High Perform Sci Comput 2011;14(9):1-9 [https://www.dlr.de/sc/portaldata/15/resources/dokumente/pyhpc2011/submissions/pyhpc2011_submission_9.pdf]

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12(85):2825-2830 [https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf]

Hunter JD. Matplotlib: A 2D graphics environment. Comput Sci Eng 2007;9(3):90-95

Waskom ML. seaborn: statistical data visualization. J Open Source Softw 2021;6(60):3021 [https://joss.theoj.org/papers/10.21105/joss.03021]

rpy2 / rpy2. GitHub. URL: https://github.com/rpy2/rpy2 [accessed 2022-09-16]

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020;17(3):261-272 [https://air.unimi.it/handle/2434/848184]

Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Red Hook, NY, US: Curran Associates Inc; 2017 Presented at: Proceedings of the 31st International Conference on Neural Information Processing Systems; December 4-9, 2017; California, USA p. 4768-4777

R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2010.

Van Rossum G, Drake FL. Python Tutorial. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica; 1995.

GitHub. URL: https://github.com/yihahn/ml_predict_prolongedMV [accessed 2023-10-17]

Clark PA, Inocencio RC, Lettieri CJ. I-TRACH: validating a tool for predicting prolonged mechanical ventilation. J Intensive Care Med 2018;33(10):567-573

Kim JH, Kwon YS, Baek MS. Machine learning models to predict 30-day mortality in mechanically ventilated patients. J Clin Med 2021;10(10):2172 [https://www.mdpi.com/resolver?pii=jcm10102172]

Jia Y, Kaul C, Lawton T, Murray-Smith R, Habli I. Prediction of weaning from mechanical ventilation using convolutional neural networks. Artif Intell Med 2021;117:102087

Parreco J, Hidalgo A, Parks JJ, Kozol R, Rattan R. Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res 2018;228:179-187

Jansen TC, van Bommel J, Schoonderbeek FJ, Sleeswijk Visser SJ, van der Klooster JM, Lima AP, et al. Early lactate-guided therapy in intensive care unit patients: a multicenter, open-label, randomized controlled trial. Am J Respir Crit Care Med 2010;182(6):752-761 [https://www.atsjournals.org/doi/10.1164/rccm.200912-1918OC]

Glasmacher SA, Stones W. Anion gap as a prognostic tool for risk stratification in critically ill patients—a systematic review and meta-analysis. BMC Anesthesiol 2016;16(1):68 [https://bmcanesthesiol.biomedcentral.com/articles/10.1186/s12871-016-0241-y]

Li F, Xin H, Zhang J, Fu M, Zhou J, Lian Z. Prediction model of in-hospital mortality in intensive care unit patients with heart failure: machine learning-based, retrospective analysis of the MIMIC-III database. BMJ Open 2021;11(7):e044779 [https://bmjopen.bmj.com/lookup/pmidlookup?view=long & pmid=34301649]

Nori N, Kashima H, Yamashita K, Ikai H, Imanaka Y. Simultaneous modeling of multiple diseases for mortality prediction in acute hospital care. New York, NY, US: Association for Computing Machinery; 2015 Presented at: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 10-13, 2015; Sydney, NSW, Australia p. 855-864

Simpson GL, Birks HJB. Statistical learning in palaeolimnology. In: Birks HJB, Lotter AF, Juggins S, Smol JP, editors. Tracking Environmental Change Using Lake Sediments: Data Handling and Numerical Techniques. Dordrecht, Netherlands: Springer; 2012:249-327

Mancini A, Vito L, Marcelli E, Piangerelli M, De Leone R, Pucciarelli S, et al. Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS". BMC Bioinformatics 2020;21(Suppl 10):347 [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03566-7]

Erenel Z, Altınçay H. Improving the precision-recall trade-off in undersampling-based binary text categorization using unanimity rule. Neural Comput Appl 2013;22(S1):83-100