Conduent Business Services, LLC

Abhishek Sengupta

Vaibhav Rajan

Prathosh Aragulla Prasad

Stephen Craig Fullerton

Katerina Sinclair

Bhupendra Singh Solanki

Satya Narayan Shukla

Sakyajit Bhattacharya

Vijay Huddar

James D Kirkendall

A method and a system for predicting admission of a human subject to a first ward in a medical center are disclosed. A patient dataset is generated based on at least a measure of one or more physiological parameters associated with one or more first human subjects and a first information pertaining to the admission of each of the one or more first human subjects to the first ward. For a first human subject of the one or more first human subjects, a first score at each of the one or more first time instants is determined. Further, one or more second time instants from the one or more first time instants are identified. Further, a second score at each of the one or more second time instants is determined. In an embodiment, the first classifier is trained based on at least the second score, and the first information.

Method and system for predicting admission of a human subject to a ward in a medical center

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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Kolkata, India

Abhishek Sengupta