RELOAD: REspiratory disease progression through LOngitudinal Audio Data machine learning

Respiratory Tract Infections (RTIs) are the most common cause of illness. This was true even before the COVID-19 pandemic. They are most often the reason patients consult a GP. The illness they cause is usually mild, but in some cases can become severe, and occasionally can lead to death. Around half of all antibiotic prescriptions are for RTIs. Most people with an RTI get better without needing treatment. However, we need to notice quickly when people are getting seriously ill. If we do not, the effect on them and on healthcare services can be large. Doctors have rules and tests that help them identify patients who are more likely to need treatment, but these do not work well for every patient. Also, they are not useful for helping patients manage their own illness. Using machine learning (AI systems) to analyse breathing and speech sounds automatically could be a game-changer. Firstly, it could reassure many patients that they do not need to see a doctor. Secondly, it could reduce prescriptions for antibiotics by identifying patients who will get better on their own. Identifying patients at higher risk could also reduce hospital admissions, cases of severe illness and the number who die. All these effects would reduce pressure on the NHS. We already know that some signs, such as breathing faster, can tell us whether an RTI is getting worse, and we know we can measure these signs by recording the sound of the breath. We know that RTIs also affect breathing pattern, the sound of speech and trying to breathe when speaking. We believe that other breathing sounds and patterns are also likely to change when you get an RTI and this is something we want to explore in this project. We aim to find information in sound recordings of breathing, cough and speech which changes in a way we can predict as a person gets sicker or recovers. We will need to research the sounds we should record and how we should analyse them to get the most useful information. A study into how these sounds change over time will give us added information, not previously explored in any great depth. We have already worked with sounds from people with COVID-19, so we know lots of people will volunteer to take part and give us their sound data if we give them an app. We know this is a very cost-effective way to study how symptoms of a disease change over time.
To be confident about using a machine learning system to treat patients, Doctors need to know if it is giving good advice. If they know a sound recording or a prediction is not very dependable, they can make sure they do extra checks or ask the patient to re-record their sounds. We plan to develop a machine learning system that can rate how reliable its own advice is each time. This will help doctors to know when to trust the system. Designing machine learning systems that can tell us about the quality of their advice is something new we will be exploring in this study.
Our project will ask volunteers to use an app to collect speech and breathing sound data. They will be asked to make a recording when they are healthy and then another one every day if they get an RTI. The app will also collect other health information from them, such as any medication they take and any other illness they may have. The machine learning system will process the data to predict whether they are getting better or worse and rate its own confidence in its prediction. GPs will use patients' medical records to tell us which of the volunteers comes to see their doctor for treatment and whether anyone had to go to hospital. This will allow us to assess the quality of the advice from the machine learning system. Our aim is to develop a machine learning system that can assess if someone with an RTI should see their doctor for advice or can expect to get better without treatment.