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

多发病是一个定义不清的概念，即人们同时患有一种以上的持续疾病。由于没有商定的报告定义，因此难以评估多重发病率的真实范围。然而，对慢性病处方和不同疾病的简单计数的分析表明，多发性硬化症正变得越来越普遍，并且与较差的结果有关，例如人们住院或过早死亡的时间。确定不同发病率发展之前的因素将有助于了解发病率是如何发展的，哪些发病率通常与其他发病率相关，为了更好地了解卫生服务和个体治疗的有效性，并确定预防或延迟这些疾病发作的机会。由于我们对这些疾病的发展知之甚少，我们建议使用新的分析方法，计算机科学，被称为机器学习，以识别不同条件之间先前隐藏或未知的关系。我们将使用安全匿名信息链接（SAIL）系统中保存的300万威尔士人医疗记录的详细信息。SAIL是一个隐私保护系统，其中已被剥夺所有个人标识符的记录可用于了解疾病的发展。我们将利用实验室调查结果的新数据，如血液化学的变化，看看这些数据是否能预测疾病的发生。如果我们确实发现了有用的模式，我们将把这些知识提供给NHS组织，让他们改善服务，并更早地干预，以保护人们的健康。通过将常规收集的流行病学数据大规模汇集在一起，该提案利用了快速发展的英国卫生信息环境的潜力。我们的团队包括卫生服务研究人员、计算机科学家、临床医生和公众，他们帮助制定了这一提案，并将继续参与研究和传播。

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.