Tariq Ahmad, James V. Freeman and Folkert W. Asselbergs
European Journal of Heart Failure (2018) 21(1): 86-89
We have come to rely on a high degree of speed, precision, and accuracy from the modern world. Algorithms can instantaneously map out all routes to our destination and adjust the path according to real-time traffic information; restaurant recommendations are transmitted to our devices as we walk through a neighbourhood relying on incredibly granular Global Positioning System (GPS) and personal preference data; millions of songs, books, and movies can be accessed on handheld devices with just a few clicks. How-ever, in medicine, no less a data science than other field, the norm is uncertainty, imprecision, and inaccuracy. A rather dramatic illustration of this is the case of implantable cardioverter defibrillators (ICD) with the capacity for cardiac resynchronization therapy (CRT) — despite the expense and invasive nature of the therapy, our ability to predict who will benefit from it remains archaic and inexact.
This article discusses ‘Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy’ by M. Cikeset al., published in the same journal issues (pages 74–85).
Health Data Research UK (HDR UK) London
Director Professor Harry Hemingway, Professor of Clinical Epidemiology, University College London Associate Directors Professor Carol Dezateux, Professor of Clinical Epidemiology and Health...
Health Data Research UK researchers develop innovative tools and technologies needed to unlock knowledge from complex and diverse health data, to address some of the biggest health challenges that...
Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach
21 June 2022
People with severe mental illness have a lower life expectancy and a higher risk of physical conditions. To improve how these comorbidities can be detected and predicted, researchers have used...