So I'm in the situation where I know what it is I'm trying to find, but not the terminology for it and I think that's why a lot of my google searches are directing me in the wrong direction, so apologies if some of this explanation ends up redundant.
Essentially, I want to be able to incorporate historical trends into the lightgbm model I've been using. Basically I have a bunch of categorical health data currently but by default, currently the model only checks each value in isolation. For examples, let's take blood pressure (BP). Currently, the model only gets the single BP measurement per row. So while it knows the historical values in its training set, it doesn't (as far as I understand) take into account trends (like BP going up over the last 5 encounters)
A lot of my preliminary research directed me towards using time series forecasting, but that's not quite what I want. I'm not trying to extrapolate what the next blood pressure measure is based on the trend (which WOULD be time series forecasting), I'm trying to incorporate 'blood pressure is trending upwards, thus the person is more at risk'.
I've read about converting time series into tabular and lagged values, but all so far seem to be to predict the next value in a time series, rather than being able to incorporate trends over time AS a feature. I think the right approach might be something like, changing BP from a single value into a window of values (bp over the last 5 encounters for example) for each row, but that's me guessing from cobbling together what I've read.
Or perhaps I'm just misunderstanding and this isn't the right approach at all. Or if lightgbm isn't suited for this (I know xgboost has problems with extrapolation and it's in a similar gradient boosting family)