#StackBounty: #classification #neural-networks #predictive-models #lstm #rnn Binary target prediction using LSTM with sparse events in …

Bounty: 50

I have a data of patients that have multiple events happening in there medical history, I’d like to predict a target of having a specific targeted-event in the next 30 days.

The data is timestamped but the time frequency is irregular and the events happens once in a while. For each patient I have:

|Date       | event_1    | test_lab_1 | event_2 | ... |Target_event_in_next_month |
-----------------------------------------------------------------------------------
|2017-01-01 | NaN        |  0.89      |  NaN    | ... |  0                        |
-----------------------------------------------------------------------------------
|2017-01-10 | 1          |  NaN       |  NaN    | ... |  0.                       |
-----------------------------------------------------------------------------------
|2017-03-01 | NaN        |  1.5       |  NaN    | ... |  1                        |
-----------------------------------------------------------------------------------
|2017-07-21 | NaN        |  NaN       |  1      | ... |  0                        |

What I could like to know is: Knowing that I’ve aggregated the data per month, is this kind of data (sparse and mix of binary and float features) suitable/compatible with LSTMs ? What would be the right strategy to solve this kind of problem ?


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