Machine Learning in Medicine
Carlos Santolaria
Abstract
Machine learning is something relatively new in medicine. As it can be seen on Pubmed, the vastmajority of articles that have been published regarding this method are not older than ten years.This article gives a brief explanation of current trends and unsolved problems in the field ofmachine learning applied to medicine. Firstly, it is necessary to highlight the difference betweensupervised machine learning and unsupervised one. The first allows the performance ofpredictions of a known target, as it is the case of the Framingham Risk Score, an algorythm bywhich it is posible to predict the probability of a Cardiovascular Heart Disease in a more or lessreliably way. On the other hand, unsupervised machine learning only allows the user to identifypatterns in a dataset, which might be helpful as approaches to therapy.As it is stated in Deo et al.’s article, machine learning in medicine has been poorly studiedpreviously in part due to the fact that, in order to obtain significant information (e.g., whenpredicting a disease), it is necessary to classify a set of features according to their general degreeof involvement in the disorder and rejecting those who do not seem significant, but these onesmight be relevant only in certain subgroups of patients. It is also necessary to find a modelflexible enough to minimize these effects.Moreover, it is important to understand that machine comprises two phases: a training one,performed via a set of examples with the aim of fit the parameters in the model, and a test, toevaluate the calibrated model. In any case, it is essential to ensure that the input informationincludes every single feature needed for the model.Anyway, a complex model might not be the best option when few training samples are available(as its generalization capacities are not adequate) or when the relationship beetweeen thefeatures studied and the expected result is simple.One example of what has previously been explained is C-path, a very interesting approachobtained by using machine learning in the Stanford University, where this method was a key toolin the calculation of breast cancer possibilities, as this model is based on 6642 tissular predictorsthrough image processing, which means this enabled researchers to know a patient’s breastcancer possibilities from an image of this patient’s interior by using computer technology to spotconcrete features on affected tissues. Despite being a simple model, it had satisfactory results.1. Deo RC. Machine Learning in Medicine. Circulation. 17 de noviembre de2015;132(20):1920-30.