How to Prepare data for LSTM
Welcome to the community.
About your problem, you say that your last column inhospital_mortality is binary so your goal is to classify, at a certain time with some info about them, patients likely to die in hospital if I am right.
Assuming this is your goal, I think you can try a classification approach without reshaping your dataset to a time series format, since you are not interested in forecasting a sequence (or a point in that sequence), you have independent buckets of x measurements per patient, without temporal connection in your training history.
You can use certain lag values (the -1, 2, 3... you already have) as informative features, and proceed with a binary classifier. Other option is, with this approach and per patient, calculate some statistical values like min, max, mean... of the 8 measurements for each type of patient indicator, while keeping the same number of rows but aggregating the attributes.
Of course you can also reshape it by having a unique date-time index with a value for each attribute type as you say, but you end up with less input features per row to predict your inhospital_mortality target value.
If you are interested in actually forecasting and eventually use an LSTM as you suggest, you might want to have a look at other answers about this type of problem like this