System and method for predicting acute, nonspecific health events
First Claim
1. A method for predicting whether an acute, nonspecific health event has or will onset in a patient, the method comprising:
- providing a computational system having both input and output devices for communicating to and from the computational system, respectively;
defining a class of acute, nonspecific events;
selecting a time interval for collecting a time series of data from the patient;
selecting at least one indicia covariate into which the time series of data is transformed for inputting into the computational system;
implementing in the computational system a Bayesian random effects model having a linear regression component, for predicting an onset of an event from the defined class of events;
employing the computational system to construct at least one probability density function and deliver at least a probability with respect to whether an event from the defined class of events has or will onset; and
communicating to the patient or a health care provider or both information delivered by the computational system and related to the predicting.
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Abstract
A patient monitoring system and method for predicting acute, nonspecific health events uses a statistical random effects model having a linear regression component. The system and method use the model to ascertain trends and/or levels in a patient'"'"'s health over short periods of time to predict whether an event from a class of acute, nonspecific events has or will onset. The system and method also include a computational system, at least one covariate that is clinically relevant to the class, and data collected from the patient. Preferably, the statistical model is a hierarchical Bayesian model having two stages of prior distributions.
46 Citations
25 Claims
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1. A method for predicting whether an acute, nonspecific health event has or will onset in a patient, the method comprising:
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providing a computational system having both input and output devices for communicating to and from the computational system, respectively;
defining a class of acute, nonspecific events;
selecting a time interval for collecting a time series of data from the patient;
selecting at least one indicia covariate into which the time series of data is transformed for inputting into the computational system;
implementing in the computational system a Bayesian random effects model having a linear regression component, for predicting an onset of an event from the defined class of events;
employing the computational system to construct at least one probability density function and deliver at least a probability with respect to whether an event from the defined class of events has or will onset; and
communicating to the patient or a health care provider or both information delivered by the computational system and related to the predicting. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for predicting whether an acute, nonspecific health event has or will onset in a patient, the method comprising:
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providing a computational system having both input and output devices for communicating to and from the computational system, respectively;
defining a class of acute, nonspecific events;
implementing in the computational system a statistical random effects model having a linear regression component, for predicting an onset of an event from the defined class of events;
employing the computational system to construct at least one probability density function and deliver at least a probability with respect to whether an event from the defined class of events has or will onset; and
communicating information delivered by the computational system and related to the predicting. - View Dependent Claims (10)
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11. A patient monitoring system for predicting whether an event from a class of acute, nonspecific health events has or will onset in a patient, the system comprising:
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a Bayesian random effects model having a linear regression component and using at least one indicia covariate that is clinically relevant to the class;
at least one time series of data related to the at least one indicia covariate and collected from the patient during a time interval preceding the predicting; and
a computational system to implement the Bayesian model and utilize the at least one time series of data to construct at least one probability density function and deliver at least a probability with respect to whether an event from the class of events has or will onset. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A patient monitoring system for predicting whether an event from a class of acute, nonspecific health events has or will onset in a patient, the system comprising:
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a statistical random effects model having a linear regression component and using at least one indicia covariate that is clinically relevant to the class; and
a computational system to implement the statistical model to construct at least one probability density function and deliver at least a probability with respect to whether an event from the class of events has or will onset. - View Dependent Claims (22)
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23. A computer program for executing a computer process for predicting whether an event from a class of acute, nonspecific health events has or will onset in a patient, the computer program being storage medium readable by a computing system or embedded in a microprocessor, the computer process comprising:
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implementing a statistical random effects model having a linear regression component and using at least one indicia covariate that is clinically relevant to the class;
accepting at least one time series of data related to the at least one indicia covariate and collected from the patient during a time interval preceding the predicting;
constructing a probability density function with respect to an occurrence of a change-point within the time interval; and
utilizing the statistical model and the at least one time series of data to construct at least one other probability density function and deliver at least a probability with respect to whether an event from the class of events has or will onset. - View Dependent Claims (24)
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25. A patient monitoring system for predicting whether an event from a class of acute, nonspecific health events has or will onset in a patient, the system comprising:
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a statistical means using at least one indicia covariate that is clinically relevant to the class;
at least one time series of data related to the at least one indicia covariate and collected from the patient during a time interval preceding the predicting; and
a computational means for implementing the statistical means and utilizing the at least one time series of data to construct at least one probability density function and deliver at least a probability with respect to whether an event from the class of events has or will onset.
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Specification