The resurgence of machine learning AI has triggered the importance of collecting “personal big data” over a long period of time from wearable devices and EHRs. Collecting data from this large number of variables over a significant period of time has further induced the study on “Temporal Phenomics”, which can be a powerful approach to achieve pre-emptive and “earlier medicine”. The paper presents a methodology to make studying “Temporal Phenomics” more feasible and convenient without limitations on the number of variables and the length of time periods.
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