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Machine learning can use processes such as, for example, classification, regression, clustering, density estimation, dimensionality reduction, and topic modeling. With classification, inputs can be divided into two or more classes, and, for example, the learning system can produce a model that assigns unseen inputs to one or more of these classes. Data can be classified, for example, into “excellent”, “good”, “acceptable” or “poor” outcome, e.g. clinical outcome. Numeric values or ranges of numeric values can be assigned to different classes, for example numeric values or ranges of numeric values from a patient reported outcome measurement, from a clinical reporting system, and/or from one or more electronic measurements. With regression, outputs can be continuous rather than discrete. Regression can be used, for example, when patient outcomes are continuous. With clustering, inputs can be divided into groups. The groups cannot be known beforehand; thus, with clustering learning can be unsupervised. With density estimation, the distribution of inputs in a given space or sample can be determined. With dimensionality reduction, inputs can be simplified by mapping them into a lower-dimensional space. With topic modeling, a machine learning system can be given a list of human language documents and can be tasked to find out which documents cover similar topics.

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