A method for obtaining, by a computing device, prediction information on an action mechanism of a drug from medical images of a brain of a subject using a neutral network, the method comprising: from a plurality of medical images X 0,... X t obtained in time series, outputting first compressed data Z 0,... Z t corresponding to the plurality of medical images X 0,... X t, each of the first compressed data having a smaller size than a corresponding medical image; estimating second compressed data h final corresponding to a medical image at a next time point T, T>t, to time points at which the plurality of medical images X 0,... X t have been captured, based on the first compressed data Z 0,... Z t, wherein the estimation of the second compressed data h final comprises: outputting intermediate data h 0 by inputting first compressed data Z 0 to a recurrent neural network; outputting intermediate data h i by inputting first compressed data Z i and intermediate data h i -1 to the recurrent neural network, for a period satisfying 0<i<t, while increasing a value of i; and estimating the second compressed data h final by inputting the first compressed data Z 0,... Z t and the intermediate data h t -1 to the recurrent neural network, obtaining prediction information on the action mechanism of the drug for the brain by inputting the second compressed data h final to the neural network learned to predict the action mechanism of the drug based on the second compressed data h final. from a plurality of medical images X 0,... X t obtained in time series, outputting first compressed data Z 0,... Z t corresponding to the plurality of medical images X 0,... X t, each of the first compressed data having a smaller size than a corresponding medical image; estimating second compressed data h final corresponding to a medical image at a next time point T, T>t, to time points at which the plurality of medical images X 0,... X t have been captured, based on the first compressed data Z 0,... Z t, wherein the estimation of the second compressed data h final comprises: outputting intermediate data h 0 by inputting first compressed data Z 0 to a recurrent neural network; outputting intermediate data h i by inputting first compressed data Z i and intermediate data h i -1 to the recurrent neural network, for a period satisfying 0<i<t, while increasing a value of i; and estimating the second compressed data h final by inputting the first compressed data Z 0,... Z t and the intermediate data h t -1 to the recurrent neural network, outputting intermediate data h 0 by inputting first compressed data Z 0 to a recurrent neural network; outputting intermediate data h i by inputting first compressed data Z i and intermediate data h i -1 to the recurrent neural network, for a period satisfying 0<i<t, while increasing a value of i; and estimating the second compressed data h final by inputting the first compressed data Z 0,... Z t and the intermediate data h t -1 to the recurrent neural network, obtaining prediction information on the action mechanism of the drug for the brain by inputting the second compressed data h final to the neural network learned to predict the action mechanism of the drug based on the second compressed data h final.