Respire - Responsible Explainable Machine Learning for Sleep-related Respiratory Disorders

Devices, like smart-watches, that can collect health data from "everybody" all the time, and machine learning (ML) to analyze this data will strongly impact future health solutions.

They can enable low-cost large-scale screening and long-term monitoring of individuals to automatically detect changes in their health status, for early detection of undiagnosed diseases, and to personalize treatment of patients. If applied without reflection there are also substantial challenges, like (1) protection and control of use of collected data, (2) false alarms, health anxiety, overdiagnosis, subsequent overtreatment, and medicalization, (3) reliability, relevance, and validity of data analysis results, and (4) inability to explain results obtained with modern ML. This undermines basic ethical principles and legal rights and may hamper fruitful use of ML in the health sector. These challenges and opportunities will be addressed by researchers from computer science, medicine, law, and ethics.