Tensor-Based Temporal Behavior Analysis in Pain Medicine

Electronic medical records provide us with an enormous amount of data with vast potential. If properly analyzed, medical data can be converted to knowledge that improves treatment, uncovers unexpected associations, and supports the personal experience of doctors and nurses, allowing them to make more informed decisions. Medical data is often generated by monitoring across a period of time, whether new data arrives quickly or slowly, consistently or sporadically, but many data mining methods are not designed to consider the temporal aspect of a data set. Besides the extra dimension of time, medical processes often involve interaction between many attributes at once, complicating the discovery of relevant patterns and associations. Specialized methods to interpret medical data can improve the quality of knowledge extracted from it. Tensors are appropriate data structures to represent our data in a multi-dimensional format, taking into account the relationship between many dimensions at once. We can further segment our data into discrete temporal chunks, creating a sequence of tensors. By applying dynamic tensor analysis to our tensor sequence, we can reveal patterns and associations within our data set and capture their change over time. This information can be developed into medical knowledge that can be used to support future treatment.