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

多重发病是一个定义不明确的概念，指人们同时患有一种以上的持续性疾病。多重发病的真正范围难以评估，因为没有商定的报告定义。然而，对慢性病处方和不同疾病的简单计数的分析表明，多病正变得越来越普遍，并且与较差的结果有关，例如人们住院的时间或过早死亡。确定在不同疾病发生之前的因素，有助于了解疾病是如何发展的，哪些疾病通常与其他疾病有关，有助于更好地了解保健服务和个别治疗的有效性，并有助于确定预防或延迟这些疾病发生的机会。由于我们对这些条件的发展知之甚少，我们建议使用计算机科学的新分析方法，即机器学习，来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300万威尔士人的医疗记录中的详细信息。SAIL是一种隐私保护系统，在该系统中，所有个人标识符都被剥离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，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地汇集在一起，该提案利用了快速发展的英国卫生信息学环境的潜力。我们的团队包括卫生服务研究人员、计算机科学家、临床医生和公众成员，他们帮助制定了这项建议，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.