Conduent Business Services, LLC

Satya Narayan Shukla

Vaibhav Rajan

Prathosh Aragulla Prasad

Stephen Craig Fullerton

Abhishek Sengupta

Katerina Sinclair

Bhupendra Singh Solanki

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 &#x3b8;=(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&#x3c;t&#x3c;N. The historical data is then updated with the forecasted observation yor the imputed unknown observation y.

Forecasting a patient vital measurement for healthcare analytics

Systems and methods of modeling irregularly sampled time series signals with unknown temporal dynamics are disclosed wherein a temporal difference variable (TDV) is introduced to model irregular time differences between subsequent measurements. A hierarchical model is designed comprising two linear dynamical systems that model the effects of evolving TDV on temporal observations. All the parameters of the model, including the temporal dynamics, are statistically estimated using historical data.

System and method of modeling irregularly sampled temporal data using Kalman filters

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Uttar Pradesh, India

Satya Narayan Shukla