Machine learning for prediction of postoperative complications after hepato-biliary and pancreatic surgery

Abstract

Machine Learning (ML) relates to the use of computer-derived algorithms and systems to enhance knowledge in order to facilitate decision making. In surgery, ML has the potential to shape clinical decision making and the management of postoperative complications in three ways: (a) by using the predicted probability of postoperative complications or survival to determine and guide optimal treatment; (b) by identifying anomalous data and patterns representing high-risk physiological states during the perioperative period and taking measures to minimise the impact of the existing risks; (c) to facilitate post-hoc identification of physiological trends, phenotypic patient characteristics, morphological characteristics of diseases, and human factors that may help alert surgeons to relevant risk factors in future patients. The accuracy, validity and integrity of data that are input into ML predictive models are central to its future success. ML could reduce errors by drawing attention to known risks of complications through supervised learning, and gain greater insights by identifying previously under-appreciated aspects of care through unsupervised learning. The success of ML in enhancing patient care will be determined by the human potential to incorporate data science techniques into daily clinical practice.