Systems and methods for transitioning patient care from signal based monitoring to risk based monitoring
First Claim
1. A risk:
- based monitoring system for transforming measured data, including heart rate and blood oxygen, of a critical care patient into hidden internal state data that is not directly measurable with sensors, the hidden internal state being monitored by the system, the system comprising;
a processor;
a memory coupled to the processor;
a display operably coupled to the processor;
a data reception module, the data reception module having a set of inputs for a plurality of sensors including at least a heart rate sensor and an SpO2 sensor, the plurality of sensors being couplable to the critical care patient, wherein the plurality of sensors provide, to the data reception module, data associated with a corresponding plurality of internal state variables ms, S=1, . . . , n, over a series of time steps tk, K=0, 1, . . . Z each internal state variable ms characterizing a parameter physiologically relevant to at least one of a treatment and a condition of the patient;
a physiology observer module, in communication with the data reception module, the physiology observer module configured toupdate, via a first and second computer processes, the data provided by the sensors to the data reception module, wherein;
the first computer process generates a conditional likelihood kernel for the internal state variables mS at time tk, the conditional likelihood kernel comprising a set of probability density functions, each such probability density function being for a distinct internal state variable mS at time tk; and
the second computer process generates,using Bayes theorem, posterior predicted conditional probability density functions for each of the internal state variables ms for the time step tk given the conditional likelihood kernel for the internal state variables at time tk and a predicted conditional probability density function of each of the internal state variables for time tk; and
a clinical trajectory interpreter module, in communication with the physiology observer module, configured to determine, based on the generated posterior predicted conditional probability density functions of the internal state variables ms at time step tk, a set of possible states of a hidden internal state variable to generate a probability value representing the likelihood that the patient'"'"'s oxygen delivery, which cannot be directly measured, is inadequate; and
a user interaction module configured to;
display, on the display device, a plurality of graphical indicators, each of the plurality of graphical indicators corresponding to one of the states of the set of possible states of the hidden internal state variable, each of the plurality of graphical indicators graphically identifying the probability that the hidden internal state variable is in a corresponding state at a given point in a range of time, the plurality of graphical indicators configured to indicate a hazard level.
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Accused Products
Abstract
A risk-based patient monitoring system for critical care patients combines data from multiple sources to assess the current and the future risks to the patient, thereby enabling providers to review a current patient risk profile and to continuously track a clinical trajectory. A physiology observer module in the system utilizes multiple measurements to estimate Probability Density Functions (PDF) of a number of Internal State Variables (ISVs) that describe a components of the physiology relevant to the patient treatment and condition. A clinical trajectory interpreter module in the system utilizes the estimated PDFs of ISVs to identify under which probable patient states the patient can be currently categorized and assign a probability value that the patient will be in each of the identified states. The combination of patient states and their probabilities is defined as the clinical risk to the patient.
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Citations
8 Claims
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1. A risk:
- based monitoring system for transforming measured data, including heart rate and blood oxygen, of a critical care patient into hidden internal state data that is not directly measurable with sensors, the hidden internal state being monitored by the system, the system comprising;
a processor; a memory coupled to the processor; a display operably coupled to the processor; a data reception module, the data reception module having a set of inputs for a plurality of sensors including at least a heart rate sensor and an SpO2 sensor, the plurality of sensors being couplable to the critical care patient, wherein the plurality of sensors provide, to the data reception module, data associated with a corresponding plurality of internal state variables ms, S=1, . . . , n, over a series of time steps tk, K=0, 1, . . . Z each internal state variable ms characterizing a parameter physiologically relevant to at least one of a treatment and a condition of the patient; a physiology observer module, in communication with the data reception module, the physiology observer module configured to update, via a first and second computer processes, the data provided by the sensors to the data reception module, wherein; the first computer process generates a conditional likelihood kernel for the internal state variables mS at time tk, the conditional likelihood kernel comprising a set of probability density functions, each such probability density function being for a distinct internal state variable mS at time tk; and the second computer process generates, using Bayes theorem, posterior predicted conditional probability density functions for each of the internal state variables ms for the time step tk given the conditional likelihood kernel for the internal state variables at time tk and a predicted conditional probability density function of each of the internal state variables for time tk; and a clinical trajectory interpreter module, in communication with the physiology observer module, configured to determine, based on the generated posterior predicted conditional probability density functions of the internal state variables ms at time step tk, a set of possible states of a hidden internal state variable to generate a probability value representing the likelihood that the patient'"'"'s oxygen delivery, which cannot be directly measured, is inadequate; and a user interaction module configured to; display, on the display device, a plurality of graphical indicators, each of the plurality of graphical indicators corresponding to one of the states of the set of possible states of the hidden internal state variable, each of the plurality of graphical indicators graphically identifying the probability that the hidden internal state variable is in a corresponding state at a given point in a range of time, the plurality of graphical indicators configured to indicate a hazard level. - View Dependent Claims (2, 3, 4, 5, 6)
- based monitoring system for transforming measured data, including heart rate and blood oxygen, of a critical care patient into hidden internal state data that is not directly measurable with sensors, the hidden internal state being monitored by the system, the system comprising;
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7. A method of transforming measured data, including heart rate and blood oxygen, of a critical care patient into hidden internal state data which is not directly measurable with sensors, the method comprising:
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providing a plurality of sensors including at least a heart rate sensor and an SpO2 sensor, the plurality of sensors being configured to be physically attachable with the critical care patient; attaching the plurality of sensors to the patient; substantially continuously acquiring, by a computer, from the plurality of sensors connected with the patient, physiological data associated with a corresponding plurality of internal state variables ms, S=1, . . . n, over a series of time steps tk, K=0, 1, . . . Z; generating, by the computer, a conditional likelihood kernel for the internal state variables ms at time tk, the conditional likelihood kernel comprising a set of probability density functions of the internal state variables ms for the time step tk, each of the internal state variables describing a parameter physiologically relevant to at least one of a treatment and a condition of said patient at time step tk; generating, with the computer and using Bayes theorem, posterior predicted conditional probability density functions for the plurality of the internal state variables ms for the time step tk given the conditional likelihood kernel for the internal state variables ms at time tk and predicted probability density functions of each of the internal state variables ms for time step tk; and identifying, with the computer, from the generated posterior predicted conditional probability density functions of the internal state variables ms at time step tk, a set of possible states of a hidden internal state variable associated with oxygen delivery (DO2), and generating a probability value representing the likelihood that the patient'"'"'s oxygen delivery (DO2) is inadequate, which cannot be directly measured generating a probability value associated with the hidden internal state; and substantially continuously displaying a clinical trajectory of the patient on a graphical user interface, the user interface being configured to display the probability of the hidden internal state variable as function of a plurality of time steps. - View Dependent Claims (8)
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Specification