Application of machine learning to discover new multimorbidity phenotypes associated with poorer outcomes

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Multi-morbidity is a poorly defined concept in which people suffer from more than one ongoing condition at the same time. The true extend of multi-morbidity is difficult to assess as there is no agreed definition for reporting. However, analysis of prescribing for chronic conditions and simple counts of different illnesses show that multimorbidity is becoming more common and is associated with poorer outcomes, such as how long people stay in hospital or premature mortality. It would be helpful to identify factors that predate the development of different morbidities to help understand how morbidities develop, which ones are commonly associated with others, to better understand the effectiveness of health services and individual treatments and to identify opportunities to prevent or delay the onset of these conditions.Because we know so little about the development of these conditions we propose to use new analytical approaches from computer science, known as machine learning, to identify previously hidden or unknown relationships between different conditions. We will use detailed information from the medical records of the 3 million people of Wales held in the Secure Anonymised Information Linkage (SAIL) system. SAIL is a privacy protecting system in which records that have been stripped of all personal identifiers can be used to understand the development of diseases. We will use the availability of new data on the results of laboratory investigations, such as changes in blood chemistry, to see if these predict the onset of conditions. If we do find useful patterns we will provide this knowledge back to NHS organisations to allow them to improve their services and intervene earlier to protect people's health. By bringing together routinely collected and epidemiologic data at scale, this proposal exploits the potential of the fast-developing UK health informatics environment. Our team includes a mixture of health service researchers, computer scientists, clinical doctors and members of the public who have helped develop this proposal and will continue to be involved in the research and its dissemination.