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Fig. 1. Spectrum between conventional and digital healthcare epidemiology. Note: Any healthcare epidemiology project may be characterized across 3 main axes: the analytical approach, the data source, and the data type as illustrated for a fictive project ‘B.’ This project used nonroutine data from a cohort study and routine data (laboratory and genetic routine data) to predict hospital-acquired infections, primarily via deep learning, a set of machine-learning algorithms, which requires little human guidance for variable selection. Routine healthcare data can be defined as data that are routinely generated or collected during healthcare delivery.36 Thus, electronic medical records and administrative claims data are typical sources of routine healthcare data.36 In contrast, nonroutine healthcare data are generated or collected for a specific nonroutine purpose (eg, as part of a clinical trial). Surveillance programs frequently incorporate both routine and nonroutine data sources. Big data is a term used to describe data that make conventional data processing difficult due to their size (volume), diversity (variety), and/or update frequency (velocity).37
Breiman.19 Most statistical algorithms have been designed to work primarily on small and low-dimensional datasets. The huge and complex datasets that are available today did not exist at the time when the first statistical algorithms were built. The advent of new techniques, and the gathering of huge and complex datasets increasingly required including computational aspects in the algorithms, leading to the term machine learning. Machine learning can be broadly divided into 2 subareas:
supervised and unsupervised learning. In supervised learning, both the input data and the corresponding target values (ie, outcomes) are observed. An example is to classify patients into either diseased or healthy. A domain expert (eg, a physician) assigns an annotation (the “label”), for example, diseased or healthy, to every patient. The aim is to find the model that best distinguishes between those two classes, to either correctly assign the label diseased or healthy to new, unlabeled patients, or to identify important covariables. Problems of this kind are called classification problems (Fig. 2). Well-known classification
algorithms include variations of logistic regression, random for- ests, support vector machines, and neural networks. Which classification algorithm is best to use depends on the
data type (eg, images, text, laboratory values, and genetic data), as well as the size and the dimensionality of the data. A corre- sponding model should be carefully selected that will generalize well to unseen data and will not simply memorize the training data (a phenomenon called “overfitting”). Regression algorithms are additional, well-known, supervised machine-learning meth- ods. In regression problems, the aim is not to separate 2 (or more) classes, but to find the function which best describes the data, to predict the correct value for a new data point (Fig. 3). In unsupervised learning, the training data consists of a set of
input variables without any corresponding target values (outcome labels) that are required in supervised learning. The goal in unsupervised learning problems is to find patterns and to extract hidden structure from data, completely data driven without any expert labelling. Typical examples are clustering problems that aim to group similar data points together (Fig. 4). Examples of
Fig. 2. Illustration of a classification task; a model learns to separate diseased from healthy individuals in a 2-dimensional space.
Fig. 3. Illustration of a regression problem; a model learns the function that best fits the data points.
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