Background Asthma exacerbation is an acute or subacute episode of progressive worsening of asthma symptoms and can have a significant impact on patients’ quality of life. However, efficient methods that can help identify personalized risk factors and make early predictions are lacking. Objective This study aims to use advanced deep learning models to better predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma. Methods We proposed a novel time-sensitive, attentive neural network to predict asthma exacerbation using clinical variables from large electronic health records. The clinical variables were collected from the Cerner Health Facts database between 1992 and 2015, including 31,433 adult patients with asthma. Interpretations on both patient and cohort levels were investigated based on the model parameters. Results The proposed model obtained an area under the curve value of 0.7003 through a five-fold cross-validation, which outperformed the baseline methods. The results also demonstrated that the addition of elapsed time embeddings considerably improved the prediction performance. Further analysis observed diverse distributions of contributing factors across patients as well as some possible cohort-level risk factors, which could be found supporting evidence from peer-reviewed literature such as respiratory diseases and esophageal reflux. Conclusions The proposed neural network model performed better than previous methods for the prediction of asthma exacerbation. We believe that personalized risk scores and analyses of contributing factors can help clinicians better assess the individual’s level of disease progression and afford the opportunity to adjust treatment, prevent exacerbation, and improve outcomes.

Insulin resistance (IR) plays a crucial role in the development and progression of metabolism-related diseases such as diabetes, hypertension, tumors, and nonalcoholic fatty liver disease, and provides the basis for a common understanding of these chronic diseases. In this study, we provide a systematic review of the causes, mechanisms, and treatments of IR. The pathogenesis of IR depends on genetics, obesity, age, disease, and drug effects. Mechanistically, any factor leading to abnormalities in the insulin signaling pathway leads to the development of IR in the host, including insulin receptor abnormalities, disturbances in the internal environment (regarding inflammation, hypoxia, lipotoxicity, and immunity), metabolic function of the liver and organelles, and other abnormalities. The available therapeutic strategies for IR are mainly exercise and dietary habit improvement, and chemotherapy based on biguanides and glucagon-like peptide-1, and traditional Chinese medicine treatments (e.g., herbs and acupuncture) can also be helpful. Based on the current understanding of IR mechanisms, there are still some vacancies to follow up and consider, and there is also a need to define more precise biomarkers for different chronic diseases and lifestyle interventions, and to explore natural or synthetic drugs targeting IR treatment. This could enable the treatment of patients with multiple combined metabolic diseases, with the aim of treating the disease holistically to reduce healthcare expenditures and to improve the quality of life of patients to some extent.

No abstract