Hearing loss affects approximately 500 million people worldwide (11 million in the UK), making it the fourth leading cause of years lived with disability (third in the UK). The resulting burden imposes enormous personal and societal consequences. By impeding communication, hearing loss leads to social isolation and associated decreases in quality of life and wellbeing. It has also been identified as the leading modifiable risk factor for incident dementia and imposes a substantial economic burden, with estimated costs of more than £30 billion per year in the UK. As the impact of hearing loss continues to grow, the need for improved treatments is becoming increasingly urgent. In most cases, the only treatment available is a hearing aid. Unfortunately, many people with hearing aids don't actually use them, partly because current devices, which are little more than simple amplifiers, often provide little benefit in social settings with high sound levels and background noise. Thus, there is a huge unmet clinical need with around three million people in the UK living with an untreated, disabling hearing loss. The common complaint of those with hearing loss, "I can hear you, but I can't understand you", is echoed by hearing aid users and non-users alike. Inasmuch as the purpose of a hearing aid is to facilitate communication and reduce social isolation, devices that do not enable the perception of speech in typical social settings are fundamentally inadequate. The idea that hearing loss can be corrected by amplification alone is overly simplistic; while hearing loss does decrease sensitivity, it also causes a number of other problems that dramatically distort the information that the ear sends to the brain. To improve performance, the next generation of hearing aids must incorporate more complex sound transformations that correct these distortions. This is, unfortunately, much easier said than done. In fact, engineers have been attempting to hand-design hearing aids with this goal in mind for decades with little success. Fortunately, recent advances in experimental and computational technologies have created an opportunity for a fundamentally different approach. The key difficulty in improving hearing aids lies in the fact that there are an infinite number of ways to potentially transform sounds and we do not understand the fundamentals of hearing loss well enough to infer which transformations will be most effective. However, modern machine learning techniques will allow us to bypass this gap in our understanding; given a large enough database of sounds and the neural activity that they elicit with normal hearing and hearing impairment, deep learning can be used to identify the sound transformations that best correct distorted activity and restore perception as close to normal as possible. The required database of neural activity does not yet exist, but we have spent the past few years developing the recording technology required to collect it. This capability is unique; there are no other research groups in the world that can make these recordings. We have already demonstrated the feasibility of solving the machine learning problem in silico. We are now proposing to collect the large-scale database of neural activity required to fully develop a working prototype of a new hearing aid algorithm based on deep neural networks and to demonstrate its efficacy for people with hearing loss.

Hospitals collect a wealth of physiological data that provide information on patient health. Full use of this data is significantly limited by its complexity and by a limited mechanistic understanding of the relationship between internal physiology and external measurement. Addressing this challenge requires multidisciplinary collaboration between mathematicians developing new biomechanical models, clinicians who measure and interpret the data to treat patients, and statistical and computational scientists to bridge the two-way translation between model output and real-life data. CHIMERA is designed to foster such collaboration to generate new understanding of physiology, new methods for relating physiology to real time data, and, finally, to translate these into practice, improving outcomes for patients by supporting clinical decision making. CHIMERA will start by focusing on the most critically ill patients within hospital intensive care units: such patients have by far the most monitoring data and are most likely to benefit from improved understanding of what that data can tell us about their underlying physical state. Each year about 20,000 children and 300,000 adults in the UK need intensive care. These critically ill patients are continuously monitored at the bedside, including measurements of heart rate, breathing rate, blood pressure and other vital sign data. However, the wealth of these physiological data are not currently used to inform clinical decision making and clinicians can only really use real-time snapshots of the physiology to guide their decisions. CHIMERA will address this unmet opportunity to use individual patient physiological data to support clinical decision making, with the potential to impact on patient management across the UK and beyond. This will be achieved through a multidisciplinary Hub which brings together experts in mathematics, statistics, data science and machine learning, with unique, high volume and rich data sets from both adult and paediatric Intensive Care Units provided through embedded Project Partnerships with Great Ormond Street Hospital (GOSH) and University College London Hospital (UCLH). CHIMERA will deliver new mathematical frameworks to learn the biophysical relationships that govern the interdependencies between physiological variables, based on data sets for thousands of patients through these project partners. Clinical impact will be achieved through an extensive series of clinically-led, multidisciplinary workshops themed around specific opportunities to improve care, for example identifying deteriorating patients in advance of an adverse event such as heart attack or stroke, or advance warning systems to diagnose sepsis. These workshops will include partnering with the Alan Turing Institute (the national centre for AI and Data Science), will be open to national participation, and will provide a mechanism to fund new projects by making available seed corn funding, PhD studentships and researcher resource for new interdisciplinary teams and partnerships. CHIMERA will build new links with clinical centres, companies and academic units across the UK and internationally, expand to work with a variety of patient monitoring data, and provide dedicated support to nurture new projects, funding bids and collaborations. In this way, we will build CHIMERA to a self-sustaining, multidisciplinary and vibrant Centre for the application of mathematical and data sciences tools in patient care.