Big Dreams With Big Data! Use of Clinical Informatics to Inform Biomarker Discovery

As the complexity of biomedical data increases, so do the opportunities to leverage them to advance science and clinical care. Electronic health records form a rich but complex source of large amounts of data gathered during routine clinical care. Through the use of codified and free-text concepts identified using clinical informatics tools such as natural language processing, disease phenotyping can be performed with a high degree of accuracy. Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, and electronic devices and wearables are generating large amounts of data from various populations, cell types, and disorders (big data). However, to make these data useable for the next step of biomarker discovery, precision medicine, and clinical practice, it is imperative to harmonize and integrate these diverse data sources. In this article, we introduce important building blocks for precision medicine, including common data models, text mining and natural language processing, privacy-preserved record linkage, machine learning for predictive modeling, and health information exchange.