Abstract: Methods and apparatus for classifying subjects based on time series phenotypic data are disclosed. In one arrangement, a data receiving unit receives a set of first subject-data-units, each first subject-data-unit in the set comprising time series data representing phenotypic information about a different respective one of a plurality of subjects to be classified. A data processing unit processes the set of first subject-data-units to reduce a dimensionality of each first subject-data-unit, thereby obtaining a corresponding set of second subject-data-units having lower dimensionality than the first subject-data-units. The set of second subject-data-units is processed to cluster the second subject-data-units into a plurality of clusters. Each of one or more of the subjects is classified by determining to which cluster a second subject-data-unit corresponding to the subject belongs.

Abstract: Methods and apparatus for subtyping subjects based on phenotypic information are disclosed. In one arrangement, a data receiving unit receives a subject data unit for each of a plurality of subjects. Each subject data unit represents a plurality of different phenotypic information items about the subject. A data processing unit uses a deep learning algorithm to derive a lower dimensional representation of each subject data unit and a clustering algorithm to detect clusters of the resulting lower dimensional representations. The deep learning algorithm and clustering algorithm are implemented by a single mathematical model in which the derivation of the lower dimensional representations and the detection of the clusters are performed jointly.