Enabling Precision Health Approaches for Symptom Science Through Big Data and Data Science

Abstract

Big data and data science are essential to enabling precision health approaches for symptom science. In contrast to big data, which is defined by the attributes of the data, data science explicitly focuses on extraction of value from data. Consequently, data science requires a technical pipeline that is informed by a research question and supported by data governance policies. The pipeline typically includes sets of tools that support: (1) extraction/ingestion, (2) wrangling (pre-processing using semi-automated tools), (3) computation and analysis, (4) modeling and application, and (5) reporting and visualization. In this chapter, we describe how big data and data science can advance precision health approaches for symptom science, summarize current challenges to use of big data and data science methods, and present future research directions.