Medical Knowledge Graphs: Artificial Intelligence for Cancer Pathways

医学知识图：癌症通路的人工智能

• 批准号: 2432658
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2432658
• 关键词: Medical; Knowledge; Graphs; Artificial; Intelligence

Routinely-collected healthcare data provides a rich source of information on the real-life workings of healthcare systems, and retrospective analyses of this data can provide real insights. However, such analysis is often difficult, as the data needed to generate useful knowledge is frequently stored heterogeneously and requires significant amounts of time to curate and integrate.Knowledge Graphs (KGs), graph-structured knowledge bases that link entities using semantic relationships, are being increasingly used in the health data literature to overcome these difficulties and construct large-scale knowledge bases from a variety of sources. The idea of KGs was initially popularised by Google through the Google Knowledge Graph, introduced in 2012, and many KGs have since been adopted in academia and industry. Today, hardly any larger company is not using a KG in some way, and artificial intelligence research around KGs is receiving increased attention.In terms of their impact, KGs are appealing in our setting for several reasons. Graph databases often rely on less rigid schemas than traditional databases, facilitating the integration of heterogeneous data. Additionally, the graph-based structure of the data is often an intuitive method for representing complex contextual information, such as a sequence of clinical events, or interactions between comorbidities. KGs allow the rich domain knowledge present in a setting to be represented and reasoned over. Despite these advantages, relatively little research so far has addressed their potential applications for modelling and greater understanding of patient pathways.There are a number of key challenges in this area, in particular the fact that pathways are often defined at high level in terms of broad recommendations, so formalising them into a machine readable form is a potential issue. The process of preparing and interpreting these pathways is also currently human-intensive, and likely to be inaccurate without clinician input.Importantly, the existing state-of-the-art solutions on pathway modelling:lose relevant structural information on the links between elements of a pathway, often choosing to model a pathway as text, images, or another data representation can be difficult to explain and audit, both to the patient or clinician, which is key to building public trust in such systems do not consider multi-granular representations of pathways, which affects transparency since pre-processing steps that translate different levels of granularity are often invisible to downstream analyses rely on raw data without incorporating incorporate domain knowledge, making it harder for reasoning methods to take advantage of the rich contextual information often available. This work attempts to take the first step in overcoming some of these challenges. We build a novel Knowledge Graph on pathways in a way that:preserves structural information between its elements provides a multi-granular representation that is explainable and auditable, thus allowing clinicians to build trust into such a system takes the first step towards more advanced AI reasoning methods that include both data-based AI methods as well as knowledge-based AI methods allows to include all contextual information available, including raw data, expert domain knowledge, thus making decision based on more complete information. This project falls within the EPSRC Artificial Intelligence and Robotics, Healthcare Technologies, as well as Information and Communication Technology research area.This project is in collaboration with Elsevier.

自动收集的医疗保健数据提供了关于医疗保健系统的真实工作的丰富信息源，并且对该数据的回顾性分析可以提供真实的见解。然而，这样的分析往往是困难的，因为所需的数据，以产生有用的知识往往是异构存储，并需要大量的时间来策展和integrated.Knowledge Graphs（KG），图形结构的知识库，使用语义关系的实体链接，正在越来越多地使用在健康数据文献，以克服这些困难，并从各种来源构建大规模的知识库。幼儿园的概念最初是由谷歌通过2012年推出的谷歌知识图谱推广的，此后许多幼儿园被学术界和工业界采用。如今，几乎没有一家大公司不以某种方式使用幼儿园，围绕幼儿园的人工智能研究也越来越受到关注。就其影响而言，幼儿园在我们的环境中很有吸引力，原因有几个。图数据库通常依赖于比传统数据库更不严格的模式，从而促进了异构数据的集成。此外，基于图形的数据结构通常是一种直观的方法，用于表示复杂的上下文信息，例如一系列临床事件或合并症之间的相互作用。KG允许在一个设置中呈现的丰富的领域知识被表示和推理。尽管有这些优点，相对较少的研究，迄今已解决其潜在的应用建模和更好地了解病人pathway.There是在这一领域的一些关键挑战，特别是事实上，路径往往是在高层次上定义的广泛的建议，因此，正式将它们转化为机器可读的形式是一个潜在的问题。准备和解释这些通路的过程目前也是人力密集型的，并且在没有临床医生输入的情况下可能是不准确的。重要的是，关于通路建模的现有最先进的解决方案：- 丢失关于通路的元素之间的链接的相关结构信息，通常选择将通路建模为文本、图像或另一数据表示可能难以向患者或临床医生解释和审核，这是在这样的系统中建立公众信任的关键，不考虑通路的多粒度表示，这影响了透明度，因为转换不同粒度级别的预处理步骤通常对下游分析不可见，下游分析依赖于原始数据而不结合领域知识，使得推理方法更难利用通常可用的丰富上下文信息。这项工作试图在克服其中一些挑战方面迈出第一步。我们在路径上构建了一个新的知识图，其方式是：保留其元素之间的结构信息，提供可解释和可审计的多粒度表示，从而使临床医生能够在这样的系统中建立信任，这是迈向更先进的AI推理方法的第一步，包括基于数据的AI方法以及基于知识的AI方法，允许包括所有可用的上下文信息，包括原始数据，专家领域知识，从而根据更完整的信息做出决策。该项目福尔斯属于EPSRC人工智能和机器人、医疗保健技术以及信息和通信技术研究领域。该项目与爱思唯尔合作。