A smartphone-based application for cough counting in patients with acute asthma exacerbation

Application and research progress of artificial intelligence in allergic diseases

Artificial intelligence (AI), as a new technology that can assist or even replace some human functions, can collect and analyse large amounts of textual, visual and auditory data through techniques such as Reinforcement Learning, Machine Learning, Deep Learning and Natural Language Processing to establish complex, non-linear relationships and construct models. These can support doctors in disease prediction, diagnosis, treatment and management, and play a significant role in clinical risk prediction, improving the accuracy of disease diagnosis, assisting in the development of new drugs, and enabling precision treatment and personalised management. In recent years, AI has been used in the prediction, diagnosis, treatment and management of allergic diseases. Allergic diseases are a type of chronic non-communicable disease that have the potential to affect a number of different systems and organs, seriously impacting people's mental health and quality of life. In this paper, we focus on asthma and summarise the application and research progress of AI in asthma, atopic dermatitis, food allergies, allergic rhinitis and urticaria, from the perspectives of disease prediction, diagnosis, treatment and management. We also briefly analyse the advantages and limitations of various intelligent assistance methods, in order to provide a reference for research teams and medical staff.

Digital Health for Asthma Management: Electronic Medication Monitoring for Adherence as a Case Example

Digital health is an umbrella term for components of health care using computer platforms, software, connectivity, and sensors to augment the recording, documentation, and communication of clinical information. The functions of digital health may be viewed in three domains: (1) the repository for patient information, (2) monitoring devices, and (3) communication tools. Monitoring devices have provided robust information as diagnostic and prognostic tools in office and hospital settings. In this review, as a case study, we will discuss the research and our direct clinical experience of electronic medication monitoring technology and the potential benefits to patient care, and the opportunities and perils encountered in using this approach for patients with moderate to severe asthma, including issues related to patient uptake and concerns for bias, impacts on the provider-patient relationship, and discussions regarding monitoring of rescue medication use in exacerbations. Additionally, although there is evidence for improvements in various aspects of patient care afforded by electronic medication monitoring, these devices have not yet seen widespread uptake in clinical settings, and we will discuss the steps needed to address these barriers and keep these important devices available for patient use in the future.

Explainable artificial intelligence for cough-related quality of life impairment prediction in asthmatic patients

Explainable Artificial Intelligence (XAI) is becoming a disruptive trend in healthcare, allowing for transparency and interpretability of autonomous decision-making. In this study, we present an innovative application of a rule-based classification model to identify the main causes of chronic cough-related quality of life (QoL) impairment in a cohort of asthmatic patients. The proposed approach first involves the design of a suitable symptoms questionnaire and the subsequent analyses via XAI. Specifically, feature ranking, derived from statistically validated decision rules, helped in automatically identifying the main factors influencing an impaired QoL: pharynx/larynx and upper airways when asthma is under control, and asthma itself and digestive trait when asthma is not controlled. Moreover, the obtained if-then rules identified specific thresholds on the symptoms associated to the impaired QoL. These results, by finding priorities among symptoms, may prove helpful in supporting physicians in the choice of the most adequate diagnostic/therapeutic plan.