Abstract: 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.

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: Systems and methods for predicting stock on hand for predefined markdown plans are provided. An example method can include retrieving retail item sales data; aggregating normal sales and markdown sales; converting normal sales and markdown sales to a weekly time series normal sales and a weekly time series markdown sales; creating a plurality of disruptive time series; receiving one or more markdown plans; performing prediction on each disruptive time series; obtaining an average of predictions from each disruptive time series to find a final sales prediction; calculating a predicted stock on hand; and rerunning the disruptive time series model to automatically recalculate the predicted stock on hand and the predicted incremental impact in real time when the processor receives a change made on the markdown plans.

Abstract: 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.

Abstract: Methods and systems for tracking a target vehicle. GPS data can be obtained from multiple vehicles in the vicinity of a target vehicle. Such GPS data can include GPS signals associated with the multiple vehicles and GPS signals associated with the target vehicle. The GPS signals associated with the multiple vehicles can be fused, and a prediction made about the travel time of the target vehicle based on the GPS signals associated with multiple vehicles and the GPS signals associated with the target vehicle. The GPS data provides a redundancy that increases the accuracy and robustness of the prediction.

Abstract: The disclosed embodiments illustrate methods of data processing for real-time prediction of crowdedness in vehicles in transit. The method includes receiving a current location of a vehicle, a real-time traffic information along a route of transit, and a current passenger demand at a first subsequent station and a second subsequent station. The method includes predicting a dwell time for the vehicle corresponding to the first subsequent station. The method includes predicting an arrival time instant of the vehicle at the second subsequent station based on a predicted first travel time of the vehicle, a predicted second travel time of the vehicle, and the predicted dwell time. The method includes predicting a passenger occupancy of the vehicle at the predicted arrival time instant at the second subsequent station based on at least a first passenger demand, a second passenger demand associated with the second subsequent station, and a passenger alighting pattern.

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.

Abstract: 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.

Abstract: 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.

Abstract: A method and a system are provided for prediction of an outcome of a stroke event associated with a first human subject. The method receives a first score, one or more first observations, and one or more second observations associated with the first human subject. The method predicts one or more second scores at the second time instant based on a training of a probabilistic model. The method further selects a second score from the one or more second scores at the second time instant. The second score corresponds to the outcome of the stroke event associated with the first human subject. The second score corresponds to the highest value from the one or more second scores.

Abstract: Compositions and methods for increasing resilience to the effects of stress are provided.