Forecasting a patient vital measurement for healthcare analytics

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 {y, . . . , y}, where ydenotes the jobservation at time t, 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 θ=(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 zwhich, in turn, is used to forecast an unknown observation yor to impute an unknown observation y, where 1<t<N. The historical data is then updated with the forecasted observation yor the imputed unknown observation y.