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 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: 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: An improved method and apparatus for automatically aligning probe pins to the test or bond pads of semiconductor devices under changing conditions. In at least one embodiment, a dynamic model is used to predict an impact of changing conditions to wafer probing process. This reduces the need for frequent measurements and calibrations during probing and testing, thereby increasing the number of dice that can be probed and tested in a given period of time and increasing the accuracy of probing at the same time. Embodiments of the present invention also make it possible to adjust positions of probe pins and pads in response to the changing conditions while they are in contact with each other.