Biomedical informatics and data science: evolving fields with significant overlap

Abstract

Big data and data science investigations hold great promise for making efficient use of data generated in the course of daily life: from social media transactions, news, and a variety of apps used by a large portion of the world’s population, including data generated for health care and life sciences research. Data science brings new insights when large-scale datasets are brought together to characterize and address complex problems. The past decade has seen a plethora of federal and private investments in biomedical data science collection, organization, and analysis, including the National Institutes of Health’s Big Data to Knowledge program, the Patient-Centered Outcomes Research Institute’s PCORnet, and investments from various industries. The work is maturing and interesting, and exciting results are emerging. Biomedical data science offers new and powerful tools to better understand health and disease through insights gleaned from data. Linking data science advances with knowledge representation and clinical information understanding, which have been traditional topics in the biomedical informatics field since its early days, has the potential to accelerate data-driven discovery. Biomedical informatics has also been addressing data-driven discovery. However, until this decade, examples where big data were available for this type of pursuit were limited. Biomedical informatics has thus evolved and overlaps significantly with biomedical data science, the subfield of data science that is concerned with discoveries using primarily clinical and other health-relevant data. All data science investigations must address important and interesting questions that are relevant to the areas they are applied to, have access to comprehensible datasets, and devise and apply methods robust enough to cope with complex unstructured observations.

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