Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation. Issue 5 (May 2016)

• Record Type: Journal Article
• Authors: Kanevsky, Jonathan; Corban, Jason; Gaster, Richard; Kanevsky, Ari; Lin, Samuel; Gilardino, Mirko
• Is Part Of: Plastic and reconstructive surgery. Volume 137:Issue 5(2016:May)
• Publication Date: 2016-05
• Subjects: Surgery, Plastic -- Periodicals; 617.95205
• DOI: 10.1097/PRS.0000000000002088
• Languages: English
• ISSNs: 0032-1052

Abstract

Abstract : Summary: Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of clinically relevant health metrics, all in an effort to guide care and ensure the best possible clinical outcomes. However, as the volume and complexity of biomedical data grow, it becomes challenging to effectively process "big data" using conventional techniques. Physicians and scientists must be prepared to look beyond classic methods of data processing to extract clinically relevant information. The purpose of this article is to introduce the modern plastic surgeon to machine learning and computational interpretation of large data sets. What is machine learning? Machine learning, a subfield of artificial intelligence, can address clinically relevant problems in several domains of plastic surgery, including burn surgery; microsurgery; and craniofacial, peripheral nerve, and aesthetic surgery. This article provides a brief introduction to current research and suggests future projects that will allow plastic surgeons to explore this new frontier of surgical science. Abstract : Supplemental Digital Content is available in the text.