Improving Prediction of Risk of Hospital Admission in Chronic Obstructive Pulmonary Disease: Application of Machine Learning to Telemonitoring Data.

Background: Telemonitoring of symptoms and physiological signs has been suggested as a means of early detection of chronic obstructive pulmonary disease (COPD) exacerbations, with a view to instituting timely treatment. However, algorithms to identify exacerbations result in frequent false-positive results and increased workload. Machine learning, when applied to predictive modelling, can determine patterns of risk factors useful for improving prediction quality.

What are the current challenges of managing cancer pain and could digital technologies help?

Objectives: Pain remains a problem for people with cancer despite effective treatments being available. We aimed to explore current pain management strategies used by patients, caregivers and professionals and to investigate opportunities for digital technologies to enhance cancer pain management.

Methods: A qualitative study comprising semistructured interviews and focus groups.