Big Data and Machine Learning in Health Care

Abstract

Nearly all aspects of modern life are in some way being changed by big data and machine learning. Netflix knows what movies people like to watch and Google knows what people want to know based on their search histories. Indeed, Google has recently begun to replace much of its existing non-machine learning technology with machine learning algorithms, and there is great optimism that these techniques can provide similar improvements across many sectors. Itisnosurprisethenthatmedicineisawashwithclaims of revolution from the application of machine learning to big health care data. Recent examples have demonstrated that big data and machine learning can create algorithms that perform on par with human physicians. 1 Though machine learning and big data may seem mysterious at first, they are in fact deeply related to traditional statistical models that are recognizable to most clinicians. It is our hope that elucidating these connections will demystify these techniques and provide a set of reasonable expectations for the role of machine learning and big data in health care.Machine learning was originally described as a program that learns to perform a task or make a decision automatically from data, rather than having the behavior explicitlyprogrammed.However,thisdefinitionisverybroad and could cover nearly any form of data-driven approach. For instance, consider the Framingham cardiovascular risk score,whichassignspointstovariousfactorsandproduces a number that predicts 10-year cardiovascular risk. Should this be considered an example of machine learning? The answer might obviously seem to be no. Closer inspection oftheFraminghamriskscorerevealsthattheanswermight not be as obvious as it first seems. The score was originally created 2 by fitting a proportional hazards model to data frommorethan5300patients,andsothe"rule"wasinfact learnedentirelyfromdata.Designatingariskscoreasamachine learning algorithm might seem a strange notion, but this example reveals the uncertain nature of the original definition of machine learning.It is perhaps more useful to imagine an algorithm as existing along a continuum between fully human-guided vs fully machine-guided data analysis. To understand the degree to which a predictive or diagnostic algorithm can said to be an instance of machine learning requires understanding how much of its structure or parameters were predetermined by humans. The trade-off between human specificationofapredictivealgorithm'spropertiesvslearning those properties from data is what is known as the machine learning spectrum. Returning to the Framingham study, to create the original risk score statisticians and clinical experts worked together to make many important decisions, such as which variables to include in the model, therelationshipbetweenthedependentandindependent variables, and variable transformations and interactions. Since considerable human effort was used to define these properties, it would place low on the machine learning

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