The role of artificial intelligence in identifying asthma in pediatric inpatient setting.

ABSTRACT:

Background

The incidence of asthma in Chinese children has rapidly increased as a result of inadequate management. This is mainly due to the failure of many primary-level pediatricians to distinguish asthma from common respiratory diseases, such as bronchitis and pneumonia. Such misdiagnoses often lead to the abuse of antibiotics and systemic glucocorticoids. Additionally, if asthma is not diagnosed early, chronic airway inflammation results in lesions that not only hamper children's athletic abilities, but serve as the primary cause for adult chronic airway diseases, such as chronic obstructive pulmonary disease (COPD).

Methods

A number of machine learning-based models including CatBoost, Logistic Regression, Naïve Bayes, and Support Vector Machines (SVM) have been develope

SUBMITTER: Yu G 

PROVIDER: S-EPMC7723595 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature