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

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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.