Valerie Kuan, Helen C Fraser, Melanie Hingorani, Spiros Denaxas, Arturo Gonzalez-Izquierdo, Kenan Direk, Dorothea Nitsch, Rohini Mathur, Constantinos A Parisinos, R Thomas Lumbers, Reecha Sofat, Ian C K Wong, Juan P Casas, Janet M Thornton, Harry Hemingway, Linda Partridge, Aroon D Hingorani
Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49...
February 3, 2021: Scientific Reports