For analyzing complex disease longitudinal data, careful modeling temporal depen- dency can help reveal disease progression mechanisms and timely intervene to help delay and prevent disease onset. It is definitely an important direction for future reseach.
There can be several possible improvements for longitudinal data analysis. From our experimental results, we have not been able to figure out significant time-associated
patterns. One possible explanation may be that the temporal dependency with respect to the disease development may not be as strong as the other interactive effects identified in this specific dataset. On the other hand, only the rules with dynamic features having ob- servations at the same time points are grouped but more general temporal dependency and continuity at neighboring time points may need to be considered. For example, the feature
bmi_03 should be more correlated to bmi_06 rather than to bmi_24. Such dependency rela- tionships may need to be integrated into the future rule-based discovery models. With the flexibility of overlapped group LASSO, we can establish a complex interaction network by adding more general dependency relationships, which may lead to more interesting pat- terns and association findings related to disease progression. We will study these potential models in our future research.
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