System and methods for transitioning patient care from signal based monitoring to risk based monitoring
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.
1. A risk-based monitoring system for transforming measured data of a 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, the plurality of sensors being couplable to the patient, wherein the plurality of sensors provide, to the data reception module, data associated with a corresponding plurality of internal state variables m s , S=1, . . . , n, over a series of time steps t k , K=0, 1, . . . Z, each internal state variable m s 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 m s at time t k , the conditional likelihood kernel comprising a set of probability density functions, each such probability density function being for a distinct internal state variable m s at time t k ; and
the second computer process generates posterior predicted conditional probability density functions for each of the internal state variables m s for the time step t k given the conditional likelihood kernel for the internal state variables at time t k and a predicted conditional probability density function of each of the internal state variables for time t k ; 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 m s at time step t k , a set of possible states of a hidden internal state variable; and
a user interaction module configured to:
generate, for 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.
2. The system of claim 1 , wherein the user interaction module is further configured to:
generate for display, on the display device, a clinical trajectory of the patient based on the conditional probability density function of the hidden internal state variable over at least some of the series of time steps.
3. The system of claim 1 , wherein the physiology observer module compares a newly received measurement associated with an internal state variable m s at time t k with a predetermined predicted likelihood of probable measurements given previously received measurements, and does not generate a conditional probability density function for the associated internal state variable m s , if the newly received measurement is not within the predetermined predicted likelihood of probable measurements for the associated internal state variable m s .
4. The system of claim 1 , further comprising a timeline controller configured to allow a user to dynamically select a plurality of points in time over the series of time steps t k , the graphical indicators changing dynamically in response to a specification by the user of one of the plurality of points in time to display the evolution of the hidden internal state variable.
5. The system of claim 1 , wherein:
the physiology observer module is further configured to generate, at time step t k , a predicted conditional probability density function for each of the internal state variables m s for time t k+1 based on the posterior predicted probability density functions for each of the internal state variables m s for the time step t k .
6. The system of claim 5 , wherein the conditional probability density functions for the time step t k+1 are generated using the posterior conditional probability density functions for each of the internal state variables ms from a preceding time step t k , using the formula:
P ( ISVs ( t k+1 )| M ( t k ))=∫ ISVs∈ISV P ( ISVs ( t k+1 )| ISVs ( t k )) P ( ISVs ( t k )| M ( t k )) dISVs.
7. The system of claim 1 , wherein the second computer process generates posterior predicted conditional probability density functions for each of the internal state variables m s for the time step t k 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 t k using Bayes theorem.
8. A method of transforming measured data of a patient into hidden internal state data which is not directly measurable with sensors, the method comprising:
providing a plurality of sensors, the plurality of sensors being configured to be physically attachable with the 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 m s , S=1, . . . n, over a series of time steps t k , K=0, 1, . . . Z;
generating, by the computer, a conditional likelihood kernel for the internal state variables m s at time t k , the conditional likelihood kernel comprising a set of probability density functions of the internal state variables m s for the time step t k , 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 t k ;
generating, with the computer, posterior predicted conditional probability density functions for the plurality of the internal state variables m s for the time step t k given the conditional likelihood kernel for the internal state variables m s at time t k and predicted probability density functions of each of the internal state variables m s for time step t k ; and
identifying, with the computer, from the generated posterior predicted conditional probability density functions of the internal state variables ms at time step t k , a set of possible states of a hidden internal state variable;
generating a probability value associated with the hidden internal state; and
generating, for display on a graphical user interface, a clinical trajectory of the patient, the user interface being configured to display the probability of the hidden internal state variable as function of a plurality of time steps.
9. The method of claim 8 , wherein the probability value associated with the hidden internal state variable indicates risk that the patient's oxygen delivery is inadequate and the patient may be in a state of shock.
10. The method of claim 8 , further comprising:
comparing a newly received measurement associated with an internal state variable m s at time t k with a predetermined predicted likelihood of probable measurements given previously received measurements, and not generating a conditional probability density function for the associated internal state variable m s , if the newly received measurement is not within the predetermined predicted likelihood of probable measurements for the associated internal state variable m s .
11. The method of claim 8 , further comprising:
providing, for display on a graphical user interface, a timeline controller configured to allow a user to dynamically select a plurality of points in time over the series of time steps t k , the graphical indicators changing dynamically in response to a specification by the user of one of the plurality of points in time to display the evolution of the internal state variable.
12. The method of claim 8 , further comprising:
generating, at time step t k , a predicted conditional probability density function for each of the internal state variables m s for time t k+1 based on the posterior predicted probability density functions for each of the internal state variables m s for the time step t k .
13. The method of claim 12 , wherein the conditional probability density functions for the time step t k+1 are generated using the posterior conditional probability density functions for each of the internal state variables m s from a preceding time step t k , using the formula:
P ( ISVs ( t k+1 )| M ( t k ))=∫ ISVs∈ISV P ( ISVs ( t k+1 )| ISVs ( t k )) P ( ISVs ( t k )| M ( t k )) dISVs.
14. A computer program product for use on a computer system for transforming measured data of a patient into hidden internal state variable data, which hidden internal state variable data is not directly measurable with sensors, the computer program product comprising a tangible, non-transitory computer readable medium having computer-readable program code thereon, the computer-readable program code comprising:
program code for causing the computer to receive, from a plurality of sensors coupled to a patient, data associated with a corresponding plurality of internal state variables m s , S=1, . . . , n, over a series of time steps t k , K=0, 1, . . . . Z, each internal state variable m s characterizing a parameter physiologically relevant to at least one of a treatment and a condition of the patient;
program code for causing the computer 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 m s at time t k , the conditional likelihood kernel comprising a set of probability density functions, each such probability density function being for a distinct internal state variable m s at time t k ; and
the second computer process generates posterior predicted conditional probability density functions for each of the internal state variables m s for the time step t k given the conditional likelihood kernel for the internal state variables at time t k and a predicted conditional probability density function of each of the internal state variables for time t k ;
program code for causing the computer to determine, based on the generated posterior predicted conditional probability density functions of the internal state variables m s at time step t k , a set of possible states of a hidden internal state variable; and
program code for causing the computer to generate, for display on a 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.
15. The computer program product of claim 14 , further comprising program code for causing the computer to generate, for display on the display device, a clinical trajectory of the patient based on the conditional probability density function of the hidden internal state variable over at least some of the series of time steps.
16. The computer program product of claim 14 , wherein the program code for causing the computer to update the data provided by the sensors includes:
program code for causing the computer to generate to compare a newly received measurement associated with an internal state variable m s at time t k with a predetermined predicted likelihood of probable measurements given previously received measurements, and to not generate a conditional probability density function for the associated internal state variable m s , if the newly received measurement is not within the predetermined predicted likelihood of probable measurements for the associated internal state variable m s .
17. The computer program product of claim 14 , further comprising program code for causing the computer to display a timeline controller configured to allow a user to dynamically select a plurality of points in time over the series of time steps t k , the graphical indicators changing dynamically in response to a specification by the user of one of the plurality of points in time to display the evolution of the hidden internal state variable.
18. The computer program product of claim 14 , further comprising program code for causing the computer to generate, at time step t k , a predicted conditional probability density function for each of the internal state variables m s for time t k+1 based on the posterior predicted probability density functions for each of the internal state variables m s for the time step t k .
19. The computer program product of claim 18 , wherein the conditional probability density functions for the time step t k+1 are generated using the posterior conditional probability density functions for each of the internal state variables m s from a preceding time step t k , using the formula:
P ( ISVs ( t k+1 )| M ( t k ))=∫ ISVs∈ISV P ( ISVs ( t k+1 )| ISVs ( t k )) P ( ISVs ( t k )| M ( t k )) dISVs.
20. The computer program product of claim 14 , wherein the second computer process generates posterior predicted conditional probability density functions for each of the internal state variables m s for the time step t k given the conditional likelihood kernel for the internal state variables at time t k and a predicted conditional probability density function of each of the internal state variables for time t k using Bayes theorem.