Logistics, Molde University College, Molde, NO-6410 Molde, Norway.
DEI, Instituto Superior Técnico, Lisboa, 1049-001 Portugal.
Int J Environ Res Public Health. 2019 Mar 3;16(5):769. doi: 10.3390/ijerph16050769.
Medicine is a knowledge area continuously experiencing changes. Every day, discoveries and procedures are tested with the goal of providing improved service and quality of life to patients. With the evolution of computer science, multiple areas experienced an increase in productivity with the implementation of new technical solutions. Medicine is no exception. Providing healthcare services in the future will involve the storage and manipulation of large volumes of data (big data) from medical records, requiring the integration of different data sources, for a multitude of purposes, such as prediction, prevention, personalization, participation, and becoming digital. Data integration and data sharing will be essential to achieve these goals. Our work focuses on the development of a framework process for the integration of data from different sources to increase its usability potential. We integrated data from an internal hospital database, external data, and also structured data resulting from natural language processing (NPL) applied to electronic medical records. An extract-transform and load (ETL) process was used to merge different data sources into a single one, allowing more effective use of these data and, eventually, contributing to more efficient use of the available resources.
医学是一个不断变化的知识领域。每天，都有新的发现和程序在进行测试，旨在为患者提供更好的服务和更高的生活质量。随着计算机科学的发展，多个领域的生产力因实施新的技术解决方案而得到提高。医学也不例外。未来提供医疗保健服务将涉及存储和处理来自病历的大量数据（大数据），需要整合来自不同数据源的数据，用于预测、预防、个性化、参与和数字化等多种目的。数据集成和数据共享对于实现这些目标至关重要。我们的工作重点是开发一个框架流程，用于整合来自不同来源的数据，以提高其可用性潜力。我们整合了来自内部医院数据库、外部数据以及应用于电子病历的自然语言处理（NPL）产生的结构化数据。使用提取、转换和加载（ETL）流程将不同的数据源合并到一个单一的数据源中，从而可以更有效地利用这些数据，并最终有助于更有效地利用现有资源。
