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Review
. 2015 Nov 17;132(20):1920-30.
doi: 10.1161/CIRCULATIONAHA.115.001593.

Machine Learning in Medicine

Rahul C Deo  1
Affiliations
Review

Machine Learning in Medicine

Rahul C Deo. Circulation. .

Abstract

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.

Keywords: artificial intelligence; computers; prognosis; risk factors; statistics.

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Conflict of interest statement

Conflict of Interest Disclosures: None.

Figures

Figure 1
Figure 1
Machine learning overview. A. Matrix representation of the supervised and unsupervised learning problem. We are interested in developing a model for predicting myocardial infarction (MI). For training data, we have patients, each characterized by an outcome (positive or negative training examples), denoted by the circle in the right-hand column, as well as by values of predictive features, denoted by blue to red coloring of squares. We seek to build a model to predict outcome using some combination of features. Multiple types of functions can be used for mapping features to outcome (B–D). Machine learning algorithms are used to find optimal values of free parameters in the model in order to minimize training error as judged by the difference between predicted values from our model and actual values. In the unsupervised learning problem, we are ignoring the outcome column, and grouping together patients based on similarities in the values of their features. B. Decision trees map features to outcome. At each node or branch point, training examples are partitioned based on the value of a particular feature. Additional branches are introduced with the goal of completely separating positive and negative training examples. C. Neural networks predict outcome based on transformed representations of features. A hidden layer of nodes integrates the value of multiple input nodes (raw features) to derive transformed features. The output node then uses values of these transformed features in a model to predict outcome. D. The k-nearest neighbor algorithm assigns class based on the values of the most similar training examples. The distance between patients is computed based on comparing multidimensional vectors of feature values. In this case, where there are only two features, if we consider the outcome class of the three nearest neighbors, the unknown data instance would be assigned a “no MI” class.
Figure 2
Figure 2
Overview of the C-Path image processing pipeline and prognostic model building procedure. A. Basic image processing and feature construction. B. Building an epithelial-stromal classifier. The classifier takes as input a set of breast cancer microscopic images that have undergone basic image processing and feature construction and that have had a subset of superpixels hand-labeled by a pathologist as epithelium(red) or stroma (green). The superpixel labels and feature measurements are used as input to a supervised learning algorithm to build an epithelial-stromal classifier. The classifier is then applied to new images to classify superpixels as epithelium or stroma. C. Constructing higher-level contextual/relational features. After application of the epithelial stromal classifier, all image objects are subclassified and colored on the basis of their tissue region and basic cellular morphologic properties. (Left panel) After the classification of each image object, a rich feature set is constructed. D. Learning an image-based model to predict survival. Processed images from patients alive at 5 years after surgery and from patients deceased at 5 years after surgery were used to construct an image-based prognostic model. After construction of the model, it was applied to a test set of breast cancer images (not used in model building) to classify patients as high or low risk of death by 5 years. From Beck et al, Sci Transl Med. 2011;3:108ra113. Reprinted with permission from AAAS.
Figure 3
Figure 3
Schematic of model development for breast cancer risk prediction. Shown are block diagrams that describe the development stages for the final ensemble prognostic model. Building a prognostic model involves derivation of relevant features, training submodels and making predictions, and combining predictions from each submodel. The model derived the attractor metagenes using gene expression data, combined them with the clinical information through Cox regression, gradient boosting machine, and k-nearest neighbor techniques, and eventually blended each submodel’s prediction. From Cheng et al, Sci Transl Med. 2013;5:181ra50. Reprinted with permission from AAAS.
Figure 4
Figure 4
Application of unsupervised learning to HFpEF. A. Phenotype heat map of HFpEF. Columns represent individual study participants; rows, individual features. B. Bayesian information criterion analysis for the identification of the optimal number of phenotypic clusters (pheno-groups). C. Survival free of cardiovascular (CV) hospitalization or death stratified by phenotypic cluster. Kaplan-Meier curves for the combined outcome of heart failure hospitalization, cardiovascular hospitalization, or death stratified by phenotypic cluster.
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