Predictive analytics: beyond the buzz

Abstract

Electronic medical records and physiologic monitors produce unprecedented amounts of clinical data, which increasingly powerful computers may turn into novel insights through machine learning and predictive algorithms. Predictive analytics are statistical methods (e.g., random forest models and neural networks) analyzing current and historical data to make predictions about the future. They may detect specific patterns or signatures of clinical deterioration before it becomes overt, opening the door to proactive instead of reactive medicine. As a result, machine learning and predictive analytics, which are subfields of artificial intelligence, are making the buzz in medical journals, congresses, and on the web. In this article, we tried to stay away from novelty blindness to provide a brief evidence-based and balanced overview of opportunities and pitfalls in acute care medicine.

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