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Systems and Methods for Transitioning Patient Care from Signal Based Monitoring to Risk Based Monitoring

  • US 20150347704A1
  • Filed: 06/01/2015
  • Published: 12/03/2015
  • Est. Priority Date: 12/16/2011
  • Status: Active Grant
First Claim
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1. A method of risk-based monitoring of a critical care patient, the method comprising:

  • 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, physiological data from the plurality of sensors connected with the patient;

    substantially continuously estimating a clinical trajectory for the patient, the patient'"'"'s clinical trajectory being described by probabilities of possible patient states using data acquired at a subsequent time step tk+1 from at least the heart rate sensor and the SpO2 sensor attached to the patient, and posterior predicted probability density functions from a previous time step tk, by;

    generating, by the computer, predicted probability density functions of internal state variables for the time step tk+1, 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+1, wherein the predicated probability density functions are calculated using posterior estimated probability density functions for each of the internal state variables from a preceding time step tk;

    generating, with the computer and using Bayes theorem, posterior predicted probability density functions for the plurality of the internal state variables for the time step tk+1 at least by computing the conditional probability density functions of the data acquired at a time step tk+1 given the internal state variables and the predicated probability density functions of internal state variables; and

    identifying, with the computer, from the generated posterior predicted probability density functions of the internal state variables at time step tk+1, into which of a first plurality of possible patient states the patient is currently categorizable;

    generating a probability value associated with each identified possible patient state; and

    substantially continuously displaying a clinical trajectory of the patient on a graphical user interface, the user interface being configured to display the probabilities of possible patient states as function of a plurality of time steps.

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