patients with chronic obstructive pulmonary disease (copD) repeat acute exacerbations (Ae). Global initiative for chronic obstructive Lung Disease (GoLD) is only available for patients in stable phase. Currently, there is a lack of assessment and prediction methods for acute exacerbation of chronic obstructive pulmonary disease (AecopD) patients during hospitalization. to enhance the monitoring and treatment of AECOPD patients, we develop a novel C5.0 decision tree classifier to predict the prognosis of AecopD hospitalized patients with objective clinical indicators. the medical records of 410 hospitalized AECOPD patients are collected and 28 features including vital signs, medical history, comorbidities and various inflammatory indicators are selected. The overall accuracy of the proposed C5.0 decision tree classifier is 80.3% (65 out of 81 participants) with 95% Confidence Interval (CI):(0.6991, 0.8827) and Kappa 0.6054. In addition, the performance of the model constructed by C5.0 exceeds the C4.5, classification and regression tree (CART) model and the iterative dichotomiser 3 (ID3) model. The C5.0 decision tree classifier helps respiratory physicians to assess the severity of the patient early, thereby guiding the treatment strategy and improving the prognosis of patients. Chronic obstructive pulmonary disease (COPD) as a major cause of chronic morbidity and mortality is a global health threat, and will become the third leading cause of death in the world by 2030 1. In 2012, 3 million deaths caused by COPD, and the amount is equivalent to 6% of the total death that year 2. COPD is characterized by chronic bronchitis, chronic airway obstruction, and emphysema, which causes progressive, irreversible decline in lung function 3. COPD patients with acute exacerbation need to be admitted to the hospital with a mortality rate of approximately 10% 4. Inflammatory cells infiltrate the small airways, releasing destructive enzymes and inflammatory factors, leading to damage and remodeling of small airways 5. Neutrophils are the main inflammatory cells in patients with COPD, which release neutrophil elastase(NE) and myeloperoxidase(MPO) 6. Macrophages are another major inflammatory cell that produces TNF-α, IL-8,reactive oxygen species(ROS), and matrix metalloproteinases(MMP) 7. Eosinophils and lymphocytes are also widely present in the airways of patients with COPD, but their mechanisms of damage to the airways are still controversial 8. However, early intervention on these patients with COPD can reduce morbidity and mortality 9,10. The socioeconomic burden caused by the deterioration of COPD cannot be underestimated 11. Global Initiative for Chronic Obstructive Lung Disease (GOLD) has been employed to classify the severity of COPD patients since 2011 12. However, GOLD is unable to fully guide the clinical treatment of COPD patients due to the complexity of the progression of COPD, exacerbation recovery time, risk of re-admission and intensive care unit (ICU) admission 13-15. In order to accurately classify the severit...

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Background The overcrowding of hospital outpatient and emergency departments (OEDs) due to chronic respiratory diseases in certain weather or under certain environmental pollution conditions results in the degradation in quality of medical care, and even limits its availability. Objective To help OED managers to schedule medical resource allocation during times of excessive health care demands after short-term fluctuations in air pollution and weather, we employed machine learning (ML) methods to predict the peak OED arrivals of patients with chronic respiratory diseases. Methods In this paper, we first identified 13,218 visits from patients with chronic respiratory diseases to OEDs in hospitals from January 1, 2016, to December 31, 2017. Then, we divided the data into three datasets: weather-based visits, air quality-based visits, and weather air quality-based visits. Finally, we developed ML methods to predict the peak event (peak demand days) of patients with chronic respiratory diseases (eg, asthma, respiratory infection, and chronic obstructive pulmonary disease) visiting OEDs on the three weather data and environmental pollution datasets in Guangzhou, China. Results The adaptive boosting-based neural networks, tree bag, and random forest achieved the biggest receiver operating characteristic area under the curve, 0.698, 0.714, and 0.809, on the air quality dataset, the weather dataset, and weather air quality dataset, respectively. Overall, random forests reached the best classification prediction performance. Conclusions The proposed ML methods may act as a useful tool to adapt medical services in advance by predicting the peak of OED arrivals. Further, the developed ML methods are generic enough to cope with similar medical scenarios, provided that the data is available.

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