Abstract: The present invention relates to a system, method and computer program product that is a multi-dimensional data mining environment and that operable to apply a series of temporal and relative rules (i.e., STDMn0) and is further operable in at least one of the following ways: to incorporate a framework to support temporal abstractions and relative alignments to data (i.e., STDMn0); and to derive characteristics within the data (STDMn0). The present invention may incorporate data from multiple sources, and potentially multiple centers. The analysis and alignment of the data may involve both temporal dimensions and other dimensions (or relative aspects) of the data. The present invention may further be a data mining environment that is flexible enough to permit relatively open ended queries thereby enabling, for example, the detection of trends, including trends with new dimensions, or trends based on relatively small data sets.

Abstract: The present disclosure relates to a method and system for identifying potential condition onset based upon a combination of Heart Rate Variability (HRV), Respiratory Rate Variability (RRV) and/or confounding factors. The present method and system may involve data collection, a temporal abstraction (TA)-based approach, and data analysis to identify potential condition onset in patients. The present method and system may generate and amend a classification scheme to be operable to determine that a patient is facing potential condition onset. The present method and system may further be operable to provide clinical decision making support. Embodiments of the present method and system may be operable to identify trends, such as temporal patterns, and to undertake a variety of analyses of collected and/or TA data to provide indicators, and determinations of potential condition onset in patients.

Abstract: The present invention relates to a system, method and computer program product that is a multi-dimensional data mining environment and that operable to apply a series of temporal and relative rules (i.e., STDMn0) and is further operable in at least one of the following ways: to incorporate a framework to support temporal abstractions and relative alignments to data (i.e., STDMn0); and to derive characteristics within the data (STDMn0). The present invention may incorporate data from multiple sources, and potentially multiple centres. The analysis and alignment of the data may involve both temporal dimensions and other dimensions (or relative aspects) of the data. The present invention may further be a data mining environment that is flexible enough to permit relatively open ended queries thereby enabling, for example, the detection of trends, including trends with new dimensions, or trends based on relatively small data sets.

Abstract: The present invention provides a system, method and computer program for multi-dimensional temporal abstraction and data mining. The invention comprises collecting and optionally cleaning multi-dimensional data, the multi-dimensional data including a plurality of data streams; temporally abstracting the multi-dimensional data; and relatively aligning the temporally abstracted multi-dimensional data based on a shared time point of interest.