Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques

Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.

Oral montelukast treatment of preschool-aged children with acute asthma

BACKGROUND

Increased amounts of cysteinyl leukotrienes have been demonstrated in urine samples from asthmatic patients, particularly during exacerbations of asthma. Although the use of leukotriene receptor antagonists has been recommended in the treatment of chronic asthma, no guidelines are available regarding their use in the treatment of acute asthma.

OBJECTIVE

To investigate the safety and effectiveness of a 4-mg tablet of oral montelukast in addition to short-acting beta2-agonist bronchodilator as the initial treatment in mild to moderate asthma exacerbations in children between 2 and 5 years old.

METHODS

Fifty-one patients who were experiencing mild to moderate asthma exacerbation were included in a randomized, double-blind, placebo-controlled, parallel-group study. Each patient received either a 4-mg tablet of montelukast or placebo in addition to inhaled salbutamol and were followed up for 4 hours. The pulmonary index score, respiratory rate, and pulse were determined at baseline and throughout 4 hours after administration.

RESULTS

Compared with placebo, the pulmonary index scores and respiratory rates were significantly lower in the montelukast group starting at 90 minutes (P = .01). This difference persisted at 120, 180, and 240 minutes of the study (P = .008, P = .02, and P = .048, respectively). At the end of the first hour of treatment, oral steroid need was 20.8% and 38.5% in patients randomized to the montelukast and placebo groups, respectively (P = .22). Hospitalization rates were not different between the 2 treatment groups.

CONCLUSION

A single 4-mg tablet of montelukast had the potential to provide additive clinical benefit in mild to moderate acute asthma in preschool-aged children when administered concomitantly with short-acting beta2-agonist bronchodilators as the initial treatment.