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I'm having difficulties to wrap my head around how I can prepare my dataset to train an LSTM.

Below is a screenshot of a subset representation of my dataset.

enter image description here

There are several other feature not included in this screenshot. The last column is inhospital_mortality which is 0 or 1 for each row.

Each feature was taken at a certain time x. features with the same feature_1,2,3 were taken at the same time.

My idea is that I will need to break every row (sample) as the example below: So in this case, each row would become 6 new rows.

| tc_tb1 | spo2_tb1 | g1_tb1| inhospital_mortality (label 0 | 1) |
| tc_tb2 | spo2_tb2 | g1_tb2| inhospital_mortality (label 0 | 1) |
| tc_tb3 | spo2_tb3 | g1_tb3| inhospital_mortality (label 0 | 1) |
...
| tc_tb6 | spo2_tb6 | g1_tb6| inhospital_mortality (label 0 | 1) |

Am I correct here? If so, how could I accomplish this dataframe manipulation in a more straight forward way? Perhaps there's a better way to reshape turn this dataset into the format I want. I wasn't able to accomplish it.

Thank you.

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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

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  • $\begingroup$ Thank you for your response. Yes, my goal is to classify if a patiend died given certain info. I agree with you that this looks like a traditional classification approach that I could use random forest or an SVM to classify. However, I've been told to use LSTM for this task to classify and I'm confused on how I can shape my data in a way that would make sense for my LSTM model. I'm not even sure if using LSTM is any useful in this case. $\endgroup$
    – bws
    Feb 26 at 14:13
  • $\begingroup$ I have read your response and it's still unclear how I could reshape my data to use in a LSTM. $\endgroup$
    – bws
    Feb 26 at 14:20
  • $\begingroup$ First, I would wonder: how many samples do I have? Does it make sense to use a train a recurrent net in you case? Second, if you cannot achieve a good enough performance with the current approach, you must set the date time as index, to have one row per time and rest of values, so it becomes a sequence $\endgroup$
    – German C M
    Feb 26 at 14:42
  • $\begingroup$ Regardless LSTM is a good approach or not for this problem (which I would say based on this info it is not), you can have a look at the answer I posted on the answer link $\endgroup$
    – German C M
    Feb 26 at 14:50
  • $\begingroup$ I see. I have about 4.5k samples. So, in my case since I don't have a feature that has a timestamp, would I have to add a new column to input timestamp? Sorry just a bit unclear on this. $\endgroup$
    – bws
    Feb 26 at 14:54

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