Abstract: An automated system that selects an optimal combination of risk models for a target patient population. The selected combination may be monitored by clinicians to determine which patients are at greatest risk for adverse events or clinical deterioration. The system may compare risk model data for hundreds or thousands of models to data collected on a target patient population to determine which combination of models is the best fit for this target group. An illustrative selection method may minimize a cost function that measures the deviation between a model combination and desired features for an optimal combination. Illustrative factors in the cost function may include differences between the predicted risk distributions for the target group, using the model risk function, and the risk distributions for the dataset used to train the model, and correlation among risks predicted by the models in the combination.

Abstract: A system that synchronizes waveforms received over a network from one or more devices, such as medical devices. Because of network delays or losses, waveforms can arrive at varying rates and times. Precise post-synchronization of the received data, to within a few milliseconds, is needed for accurate analysis. Applications include automatic classification of waveforms, such as detection of myocardial infraction from heart monitor waveforms. Synchronization uses sequence numbers assigned by each device, but must also account for sequence number wraparounds. Waveforms may also be synchronized across devices, by calculating the bias between within-device synchronized times and a common time source or common disturbance. Waveform data may also be stored data in a database or data warehouse; embodiments may index the data using a key with a date-time prefix and a hash code suffix, to support distributed indexing while reducing the chance of hash collisions to a very small probability.

Abstract: A method for time-synchronizing waveforms from different patient monitors that does not require devices to have high-precision synchronized clocks or to be coupled to a triggering synchronization signal generator. Comparable signals may be obtained from different devices either by placing selected sensors from the devices in the same locations, or by filtering signals from one device to obtain a signal comparable to signals from another device. Filtering may for example transform waveforms into independent components and identify a component that matches a signal from another device. The comparable signals may then be transformed into frequency variation curves, such as time intervals between peak values, to facilitate detection of the time shift between the signals. Cross correlation of the frequency variation curves may be used to locate the precise time shift between the signals. Use of frequency variation curves may be more robust than directly comparing and correlating the original signals.

Abstract: A system that synchronizes waveforms received over a network from one or more devices, such as medical devices. Because of network delays or losses, waveforms can arrive at varying rates and times. Precise post-synchronization of the received data, to within a few milliseconds, is needed for accurate analysis. Applications include automatic classification of waveforms, such as detection of myocardial infraction from heart monitor waveforms. Synchronization uses sequence numbers assigned by each device, but must also account for sequence number wraparounds. Waveforms may also be synchronized across devices, by calculating the bias between within-device synchronized times and a common time source or common disturbance. Waveform data may also be stored data in a database or data warehouse; embodiments may index the data using a key with a date-time prefix and a hash code suffix, to support distributed indexing while reducing the chance of hash collisions to a very small probability.

Abstract: A system that synchronizes waveforms received over a network from one or more devices, such as medical devices. Because of network delays or losses, waveforms can arrive at varying rates and times. Precise post-synchronization of the received data, to within a few milliseconds, is needed for accurate analysis. Applications include automatic classification of waveforms, such as detection of myocardial infraction from heart monitor waveforms. Synchronization uses sequence numbers assigned by each device, but must also account for sequence number wraparounds. Waveforms may also be synchronized across devices, by calculating the bias between within-device synchronized times and a common time source or common disturbance. Waveform data may also be stored data in a database or data warehouse; embodiments may index the data using a key with a date-time prefix and a hash code suffix, to support distributed indexing while reducing the chance of hash collisions to a very small probability.