Abstract: What is disclosed is a system and method for forecasting and imputing an unknown vital measurement of a patient. Temporally successive patient vital measurements are received which comprise irregularly sampled observations {y1, . . . , yN}, where yj denotes the jth observation at time tj, and N is the number of samples. The vital measurements are then provided to a model trained using historical data of patient vital measurements. The model generates a parameter set ?=(A,B,C), where A is a state transition matrix, B is a control matrix, and C is a matrix which maps state-space variables to observation variables. The parameters are used to obtain state-space variable zt which, in turn, is used to forecast an unknown observation yN+1 or to impute an unknown observation yt, where 1<t<N. The historical data is then updated with the forecasted observation yN+1 or the imputed unknown observation yt.

Abstract: What is disclosed is a system and method for forecasting and imputing an unknown vital measurement of a patient. Temporally successive patient vital measurements are received which comprise irregularly sampled observations {y1. . . , yN where yN denotes the jth observation at time tj and N is the number of samples. The vital measurements are then provided to a model trained using historical data of patient vital measurements. The model generates a parameter set ?=(A, B, C), where A is a state transition matrix, B is a control matrix, and C is a matrix which maps state-space variables to observation variables. The parameters are used to obtain state-space variable zt which, in turn, is used to forecast an unknown observation yN+1 or to impute an unknown observation yt, where 1<t<N. The historical data is then updated with the forecasted observation yN+1 or the imputed unknown observation yt.