Personalized medicine is being realized by our ability to measure biological and environmental information about patients. Much of these data are being stored in electronic health records yielding big data that presents challenges for its management and analysis. Here, we review several areas of knowledge that are necessary for next-generation scientists to fully realize the potential of biomedical big data. We begin with an overview of big data and its storage and management. We then review statistics and data science as foundational topics followed by a core curriculum of artificial intelligence, machine learning and natural language processing that are needed to develop predictive models for clinical decision making.

HENDERSON, Nev.--(BUSINESS WIRE)--Quertle LLC has developed the first artificial intelligence and visual analytics big data (BioAI) platform for biomedical drug discovery. This platform combines machine learning, neural networks, and other AI methods to improve discovery and insight. The BioAI platform includes predictive visual analytics that accelerate search and discovery from the literature, enable discovery of documents that are otherwise overlooked and improve drug discovery. The AI-powered visualizations summarize an entire set of documents, detect trends and uncover hidden connections. Biomedical literature is the foundation of the 5 trillion health care market, serving as the basis for drug discovery, medical device opportunities, marketing decisions and direct health care.