I am trying to figure out what would be the best way to learn patterns with a data-set that has temporal dependency between its features.
Let's say I want to predict whether a patient will suffer from a heart attack in the following minute by looking at his/her heart-rate, blood pressure and oxygen levels on each minute for the past five minutes.
Now each combination of the three features stated above has no predictive value on its own, but I assume that a temporal analysis might reveal some interesting predictive ability. For example, having a slow heart rate two minutes after heaving fast heart rate and low oxygen levels might point to an increase possibility of a heart attack.
What would be the best approach to tackle this kind of problem? Would a regular ML classification work in this case if I use each combination of index*time as a feature? (resulting in 3(index)*5(time-points) features).
Any suggestions/reading materials would be appreciated.