Disease management
Medication non-adherence in allergic diseases is common in clinical practice and can negatively impact disease control. To address this issue, researchers explored ML approaches for disease management and medication adherence. One such approach involves using ML to provide early warnings for loss of control in the Asthma Mobile Health Study data of 5,875 patients, containing over 75,000 daily surveys on symptoms and medicine use, medical history, demographics, location and EuroQol 5D questionnaire. The supervised classifier obtained an AUC of 0.87, but peak flow readings did not further enhance its performance. External or prospective validation is strongly needed.
In addition to early warning systems, chatbots have also been proposed to support disease management by providing personalized advice to patients and tracking medication compliance. One example is KBot, an early prototype of a chatbot for asthma that utilizes contextual information (such as high pollen triggers) and NLP for dialogue processing. AI can also leverage the capabilities of wearables and mHealth technologies to monitor disease outside clinical contexts. A recent study tested a prototype application for real-time counting of coughs using a deep learning model on ambient sound recorded by mobile phone . This yielded accurate and real-time cough count with a specificity of 92% and a specificity of 98%. Another study applied ML to analyze the sounds of asthma inhalers to predict adequate usage and drug actuations. Recorded sound on mobile devices has also been proposed to monitor lung function in asthmatics. While requiring further validation, these techniques could be used to develop future telehealth solutions including smartphone-based applications, which have the potential to aid decision-making and self-monitoring in asthma.
Fundamental research AI can provide insights into disease classification, pathophysiology, and the underlying biological mechanisms, by clustering large numbers of data points into interpretable patterns.