Woodman Richard John, Koczwara Bogda, Mangoni Arduino Aleksander
Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.
Department of Medical Oncology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, SA, Australia.
Front Med (Lausanne). 2024 Jan 24;10:1302844. doi: 10.3389/fmed.2023.1302844. eCollection 2023.
The current management of patients with multimorbidity is suboptimal, with either a single-disease approach to care or treatment guideline adaptations that result in poor adherence due to their complexity. Although this has resulted in calls for more holistic and personalized approaches to prescribing, progress toward these goals has remained slow. With the rapid advancement of machine learning (ML) methods, promising approaches now also exist to accelerate the advance of precision medicine in multimorbidity. These include analyzing disease comorbidity networks, using knowledge graphs that integrate knowledge from different medical domains, and applying network analysis and graph ML. Multimorbidity disease networks have been used to improve disease diagnosis, treatment recommendations, and patient prognosis. Knowledge graphs that combine different medical entities connected by multiple relationship types integrate data from different sources, allowing for complex interactions and creating a continuous flow of information. Network analysis and graph ML can then extract the topology and structure of networks and reveal hidden properties, including disease phenotypes, network hubs, and pathways; predict drugs for repurposing; and determine safe and more holistic treatments. In this article, we describe the basic concepts of creating bipartite and unipartite disease and patient networks and review the use of knowledge graphs, graph algorithms, graph embedding methods, and graph ML within the context of multimorbidity. Specifically, we provide an overview of the application of graph theory for studying multimorbidity, the methods employed to extract knowledge from graphs, and examples of the application of disease networks for determining the structure and pathways of multimorbidity, identifying disease phenotypes, predicting health outcomes, and selecting safe and effective treatments. In today's modern data-hungry, ML-focused world, such network-based techniques are likely to be at the forefront of developing robust clinical decision support tools for safer and more holistic approaches to treating older patients with multimorbidity.
目前对患有多种疾病的患者的管理并不理想，要么采用单一疾病的护理方法，要么对治疗指南进行调整，但由于其复杂性，导致依从性较差。尽管这引发了对更全面、个性化处方方法的呼吁，但在实现这些目标方面进展仍然缓慢。随着机器学习（ML）方法的迅速发展，现在也有了一些有前景的方法来加速精准医学在多种疾病中的进展。这些方法包括分析疾病共病网络、使用整合来自不同医学领域知识的知识图谱，以及应用网络分析和图机器学习。多种疾病网络已被用于改善疾病诊断、治疗建议和患者预后。通过多种关系类型连接不同医学实体的知识图谱整合了来自不同来源的数据，允许复杂的相互作用，并创造了持续的信息流。然后，网络分析和图机器学习可以提取网络的拓扑结构，揭示隐藏的属性，包括疾病表型、网络枢纽和通路；预测药物的新用途；并确定安全且更全面的治疗方法。在本文中，我们描述了创建二分和单分疾病及患者网络的基本概念，并回顾了在多种疾病背景下知识图谱、图算法、图嵌入方法和图机器学习的应用。具体而言，我们概述了图论在研究多种疾病中的应用、从图中提取知识所采用的方法，以及疾病网络在确定多种疾病的结构和通路、识别疾病表型、预测健康结果以及选择安全有效的治疗方法方面的应用示例。在当今这个渴望数据、专注于机器学习的现代世界中，这种基于网络的技术很可能处于开发强大临床决策支持工具的前沿，以采用更安全、更全面的方法治疗患有多种疾病的老年患者。
