Improved time series operators

[lacava]

Computable Phenotypes can represent quite complex relations, such as this (portion of the) definition for resistant hypertension:

Has ≥ 4 simultaneous matching med classes on ≥ 2 occasions, ≥ 1 month apart.

The model must

• aggregate time series
• filter time series to minimum intervals
• select time series matching a class of drugs
• threshold the values of the time series
• threshold the counts of the time series

For this example, wewould updates to operators to get this to work:

• Sum operator that takes nary TimeSeries operators and returns a time series object
  • It would essentially be a “grouped” sum, where values are grouped by unique times and then the operator is applied
• Some sort of filter/mask operator that filters a TimeSeries’s samples based on a condition
  • Interval: returns the time between events
  • Values: returns the time series values
• FilterValues, FilterTimes, FilterIntervals
  • Signature: TimeSeries<T> → TimeSeries<T>
  • operators with a threshold (stored in w)
  • heuristic way to determine threshold? (number of samples filtered in cases versus controls?)
  • probably the thresholds would have to be determined stochastically