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
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