Using Knowledge Graph Learning to Predict and Explain Patient Outcomes in Electronic Health Records

该项目的目的是开发一种能够自动预测和解释患者结果的系统。这项研究的目的是通过分析大规模匿名电子病历来改善患者护理。例如，开发的方法可以预测一种新药将具有罕见但严重的副作用，或者特定患者组的负面治疗结果存在潜在的可预防原因。这是可能的，因为我们可以将信息表示为一个网络。网络是表示事物之间联系的一般方式，例如人与人之间的友谊、网站之间的链接或细胞内的分子反应。网络包含“节点”(事物)和“边”(事物之间的连接)。在友谊网络中，人将是节点，所有成对的朋友之间将有一条边。图论是一套数学原理，我们可以用来分析任何类型的网络，试图了解连接的结构如何与整体功能相关。在这个联谊会中，将创建一个大型网络，将公开可用的药物、疾病和细胞生物学数据与从电子医疗记录中提取的匿名数据结合起来。这种网络方法最强大的方面之一是，它允许这些不同类型的信息直接连接，并准确地表示它们彼此之间的关系。这使得计算机可以利用现有医学知识的额外背景来推理患者的结果。算法可以分析这个网络，根据已知的事物之间的联系做出预测(例如，扑热息痛被认为是止痛药，其他类似于扑热息痛的药物也可能是有效的止痛药)。虽然这些关系的含义对一个人来说通常是直观的，但开发能够应用这种类型的推理的算法是具有挑战性的。该奖学金的目的是开发这样的方法，并将其应用于临床有用的预测。工作的第一部分是结合公开可用的数据，并创建可能有助于解释患者结果的已知事实的大型网络。然后，这个网络将被用来开发和优化做出预测的算法，方法是训练它们预测已知的关联，如药物副作用或疾病风险因素。网络和算法准备就绪后，工作将从两个方向进行。首先，我们可以查看图表并预测“缺失信息”，这意味着考虑到我们所知道的所有可能导致严重副作用(例如史蒂文斯-约翰逊综合征)的药物，很可能药物A、B和C也可能导致副作用。然后通过分析匿名的电子病历来验证这些预测。该项目的第二方面是解释在医疗记录中观察到的结果。第一步是确定一种趋势，例如确定对治疗反应不佳或阴性结果比率异常高的患者群体。我们可以使用图表来预测为什么会存在这种模式，给出预测算法可用的所有医疗信息。这些模式及其预测的解释将接受医学审查，并用于为改善患者护理的政策和最佳实践决策提供信息。

The aim of this project is to develop a system that can automatically predict and explain patient outcomes. The purpose of the research is to improve patient care by analysing anonymised electronic medical records at very large scale. For example, the methods developed could predict that a new drug will have a rare but serious side effect, or that there is a potentially preventable cause of a negative treatment outcome of a specific group of patients. This is possible because we can represent information as a network. Networks are a general way to represent the connections between things, such as friendships between people, links between websites, or molecular reactions in a cell. Networks contain "nodes" (the things) and "edges" (connections between the things). In the friendship network, people would be the nodes and there would be an edge between all the pairs of people who are friends. Graph Theory is a set of mathematical principles we can use to analyse any type of network to try to understand how the structure of the connections relates to the overall function.In this fellowship, a large network will be created that combines publicly available data on medications, diseases and cell biology with anonymised data extracted from electronic medical records. One of the most powerful aspects of this network approach is that it allows these different types of information to be directly connected, and represents exactly how they relate to each other. This allows a computer to reason about patient outcomes with the extra context of existing medical knowledge. Algorithms can analyse this network to make predictions based on the known connections between things (for example, paracetamol is known to work as a painkiller, other drugs similar to paracetamol might also be effective painkillers). Whilst the meaning of these relationships is often intuitive to a person, it is challenging to develop algorithms that can apply this type of reasoning. The purpose of this fellowship is to develop such methods and apply them to make clinically useful predictions.The first part of the work is to combine the publicly available data and create the large network of known facts that could be useful to explain patient outcomes. This network will then be used to develop and optimise the algorithms that will make the predictions, by training them to predict known associations such as drug side effects or disease risk factors. With the network and the algorithms ready, the work then proceeds in two directions. Firstly, we can look at the graph and predict "missing information", meaning that given everything we know about the drugs that can cause a serious side effect (e.g. Stevens-Johnson syndrome), it's very likely that drugs A, B and C also could cause it. These predictions are then validated by analysing anonymised electronic medical records. The second side to the project is to explain outcomes that are observed in medical records. The first step there is to identify a trend, such as identifying a population of patients who respond poorly to treatment or have an unusually high rate of a negative outcome. We can use the graph to predict why this pattern exists, given all of the medical information available to the predictive algorithm. These patterns, along with their predicted explanations, will be subject to medical review and used to inform policy and best practice decisions to improve patient care.