Health informatics and biomedical computing have introduced the use of computer methods to analyze clinical information and provide tools to assist clinicians during the diagnosis and treatment of diverse clinical conditions. With the amount of information that can be obtained in the healthcare setting, new methods to acquire, organize, and analyze the data are being developed each day, including new applications in the world of big data and machine learning. In this review, first we present the most basic concepts in data science, including the structural hierarchy of information and how it is managed. A section is dedicated to discussing topics relevant to the acquisition of data, importantly the availability and use of online resources such as survey software and cloud computing services. Along with digital datasets, these tools make it possible to create more diverse models and facilitate collaboration. After, we describe concepts and techniques in machine learning used to process and analyze health data, especially those most widely applied in rheumatology. Overall, the objective of this review is to aid in the comprehension of how data science is used in health, with a special emphasis on the relevance to the field of rheumatology. It provides clinicians with basic tools on how to approach and understand new trends in health informatics analysis currently being used in rheumatology practice. If clinicians understand the potential use and limitations of health informatics, this will facilitate interdisciplinary conversations and continued projects relating to data, big data, and machine learning.
Keywords: Data analysis; Deep learning; Machine learning; Medical records analysis.