In a recent editorial, our member Pablo Lamata from King's College London describes how computers will pave the road towards the concept of the personalised medicine.
"Engineers and researchers are boosting human induction and deduction skills assisted by computers, and this is one of the main drivers of progress in cardiovascular sciences and health care. This claim builds on two core ideas: machine learning as the vehicle for inductive reasoning, and computational cardiac models as the tool for deductive reasoning.
Machine learning refers to the ability of computers to gain knowledge without being explicitly programmed for it: the computer extracts patterns from the data and thus ‘learns’ a statistical model that will perform a given task (i.e. disease classification and prediction or segment the myocardium in an image). The logic process followed here is inductive, since the machine makes broad generalizations from specific observations it has learned from. The availability of new sources of data (i.e. omics or continuous monitoring systems), the digitalization of the health record, and the recent advances in machine learning technology now offer the opportunity to reveal new patterns and new signatures of cardiovascular health and disease.
On the other hand, computational cardiac models are the representations of our knowledge of the physiology of the heart pump and circulatory system governed by fundamental laws of physics and biochemistry. Computers are explicitly programmed to represent this knowledge into mechanistic models and use them to perform a given task (i.e. estimate myocardial stiffness, identify the fibrosis patterns that lead to persistent re-entrant drivers in atrial fibrillation, or compute the risk of drug toxicity). The logic process followed here is deductive, since the machine reaches conclusions based on the concordance of multiple premises and assumptions. The availability of rich anatomical and functional data and the recent advances in computational cardiology technology now offer, through the process of model personalization, two opportunities: the ability to present an integral and cohesive diagnostic picture of the patient and the possibility to simulate and predict the evolution of a condition or the impact of a treatment.
Both statistical and mechanistic models are thus rendering very encouraging perspectives and expectations, but these should be handled with caution."
The full article is available here
