Achieving predictive, precise, participatory, preventive, and personalized health (abbreviated as p-Health) requires comprehensive evaluations of an individual's conditions captured by various measurement technologies. Since the 1950s, analysis of care providers' and physicians' notes and measurement data by computers to improve healthcare delivery has been termed clinical informatics. Since the 2010s, wide adoptions of Electronic Health Records (EHRs) have greatly improved clinical informatics development with fast growing pervasive wearable technologies that continuously capture the human physiological profile in-clinic (EHRs) and out-of-clinic (PHRs or Personal Health Records) to bolster mobile health (mHealth). In addition, after the Human Genome Project in the 1990s, medical genomics has emerged to capture the high-throughput molecular profile of a person. As a result, integrated data analytics is becoming one of the fast-growing areas under Biomedical Big Data to improve human healthcare outcomes. In this chapter, we first introduce the scope of data integration and review applications, data sources, and tools for clinical informatics and medical genomics. We then describe the data integration analytics at the raw data level, feature level, and decision level with case studies, and the opportunity for research and translation using advanced artificial intelligence (AI), such as deep learning. Lastly, we summarize the opportunities in biomedical big data integration that can reshape healthcare toward p-health.
Keywords: Biomedical big data analytics; Clinical informatics; Data integration; Genomics; Machine learning and artificial intelligence; p-Health.
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