The vision of IASIS project is to turn the wave of big biomedical data heading our way into actionable knowledge for decision makers. This is achieved by integrating data from disparate sources, including genomics, electronic health records and bibliography, and applying advanced analytics methods to discover useful patterns. The goal is to turn large amounts of available data into actionable information to authorities for planning public health activities and policies. The integration and analysis of these heterogeneous sources of information will enable the best decisions to be made, allowing for diagnosis and treatment to be personalised to each individual. The project offers a common representation schema for the heterogeneous data sources. The iASiS infrastructure is able to convert clinical notes into usable data, combine them with genomic data, related bibliography, image data and more, and create a global knowledge base. This facilitates the use of intelligent methods in order to discover useful patterns across different resources. Using semantic integration of data gives the opportunity to generate information that is rich, auditable and reliable. This information can be used to provide better care, reduce errors and create more confidence in sharing data, thus providing more insights and opportunities. Data resources for two different disease categories are explored within the iASiS use cases, dementia and lung cancer.

翻译：iASiS项目的愿景是将当前涌现的生物医学大数据浪潮转化为决策者可付诸行动的知识。这一目标通过整合来自基因组学、电子健康记录和文献目录等不同来源的数据，并应用先进的分析方法来发掘有用模式而实现。其宗旨是将海量可用数据转化为可供公共卫生管理机构用于规划活动与政策的可操作信息。对这些异构信息源进行整合与分析，将有助于做出最优决策，从而实现对个体的个性化诊断与治疗。该项目为异构数据源提供了一种通用的表示框架。iASiS基础设施能够将临床记录转化为可用数据，并将其与基因组数据、相关文献、影像数据等相结合，构建全局知识库。这为运用智能方法发掘跨资源的有用模式提供了便利。通过数据的语义整合，能够生成丰富、可审计且可靠的信息。此类信息可用于改善医疗护理、减少差错、增强数据共享的可信度，从而提供更深入的见解与机遇。在iASiS应用案例中，针对痴呆症和肺癌两类疾病的数据资源进行了探索性研究。