Long Term Active Learning from Large Continually Changing Data Sets

• US 20110282169A1
• Filed: 10/26/2009
• Published: 11/17/2011
• Est. Priority Date: 10/29/2008
• Status: Abandoned Application

• Current Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Original Assignee: The Board of Regents of the University of Colorado (University of Colorado System)
• Inventors: Moulton, Steven Lee, Grudic, Gregory Zlatko
• Application Number:
  US13/126,727
• Publication Number:
  US 20110282169A1
• Time in Patent Office: Days
• US Class Current: 600/324
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

Methods and systems are disclosed for autonomously building a predictive model of outcomes. A most-predictive set of signals S_k is identified out of a set of signals s₁, s₂, . . . , s_D for each of one or more outcomes o_k. A set of probabilistic predictive models ô_k=M_k (S_k) is autonomously learned, where ô_k is a prediction of outcome o_k derived from the model M_k that uses as inputs values obtained from the set of signals S_k. The step of autonomously learning is repeated incrementally from data that contains examples of values of signals s₁, s₂, . . . , s_D and corresponding outcomes o₁, o₂, . . . , o_K. Various embodiments are also disclosed that apply predictive models to various physiological events and to autonomous robotic navigation.

Claims

1. A method of autonomously building predictive models of outcomes, the method comprising:

identifying a most-predictive set of signals S_k out of a set of signals s₁, s₂, . . . , s_D for each of one or more outcomes o_k;

autonomously learning a set of probabilistic predictive models ô

_k=M _k(S_k), where {right arrow over (o)}_k is a prediction of outcome o_k derived from the model M_k that uses as inputs values obtained from the set of signals S_k;

repeating the step of autonomously learning incrementally from data that contains examples of values of signals s₁, s₂, . . . , s_D and corresponding outcomes o₁, o₂, . . . , o_K.

2. The method recited in claim 1 wherein autonomously learning the set of probabilistic predictive models comprises using a linear model framework to identify predictive variables for each increment of data.

3. The method recited in claim 2 wherein the linear model framework is constructed with the form

4. A system for autonomously building a predictive model of outcomes, the system comprising:

an input device; and

a processor having a computer-readable storage medium, the processor in electrical communication with the input device, the computer-readable storage medium comprising;

instructions for identifying a most-predictive set of signals S_k out of a set of signals s₁, s₂, . . . , S_D for each of one or more outcomes o_k;

instructions for autonomously learning a set of probabilistic predictive models ô

_k=M_k(S_k), where ô

_k is a prediction of outcome o_k derived from the model M_k that uses as inputs values obtained from the set of signals S_k;

instructions for repeating the step of autonomously learning incrementally from data that contains examples of values of signals s₁, s₂, . . . , s_D and corresponding outcomes o₁, o₂, . . . , o_K.

5. The system recited in claim 4 wherein the instructions for autonomously learning the set of probabilistic predictive models comprise instructions for using a linear model framework to identify predictive variables for each increment of data.

6. The system recited in claim 5 wherein the linear model framework is constructed with the form

7. A method of predicting volume of acute blood loss from a patient, the method comprising:

collecting data values from one or more physiological sensors attached to the patient; and

applying a hemodynamic compensation model to the collected data values to predict the volume of acute blood loss from the patient,

wherein the hemodynamic compensation model is generated from a plurality of data values collected from physiological sensors attached to a plurality of subjects.

8. The method recited in claim 7 wherein the one or more physiological sensors comprises a sensor selected from the group consisting of a blood pressure monitor, a noninvasive blood pressure monitor, an electrocardiograph, a pulse oximeter, a transcranial Doppler sensor, an impedance cardiograph, a finometer, an infrared spectrometer, a capnography sensor, and a photoplethysmograph.

9. The method recited in claim 7 wherein the collected data values comprise data values selected from the group consisting of a perfusion index, a pleth variability index, cardiac output, heart stroke volume, arterial blood pressure, systolic blood pressure, diastolic blood pressure, mean arterial pressure, systolic pressure variability, pulse pressure, pulse pressure variability, stroke volume, cardiac index, and near-infrared spectroscopy data.

10. A system for predicting volume of acute blood loss from a patient, the system comprising:

one or more physiological sensors attached to the patient to collect data values; and

a computational unit in communication with the one or more physiological sensors and having instructions to apply a hemodynamic compensation model to the collected data values to predict the volume of acute blood loss from the patient,

wherein the hemodynamic compensation model is generated from a plurality of data values collected from physiological sensors attached to a plurality of subjects.

11. The system recited in claim 10 wherein the one or more physiological sensors comprises a sensor selected from the group consisting of an electrocardiograph, a pulse oximeter, transcranial Doppler sensor, capnography sensor, and a photoplethysmograph.

12. The system recited in claim 10 wherein the collected data values comprise data values selected from the group consisting of a perfusion index, a pleth variability index, cardiac output, heart stroke volume, arterial blood pressure, systolic blood pressure, diastolic blood pressure, mean arterial pressure, systolic pressure variability, pulse pressure, pulse pressure variability, stroke volume, cardiac index, and near-infrared spectroscopy data.

13. A method of predicting volume of acute blood loss from a patient that will cause cardiovascular collapse, the method comprising:

collecting data values from one or more physiological sensors attached to the patient; and

applying a hemodynamic compensation model to the collected data values to predict the volume of acute blood loss from the patient that will cause CV collapse,

wherein the hemodynamic compensation model is generated from a plurality of data values previously collected from physiological sensors attached to a plurality of subjects.

14. The method recited in claim 13 wherein the one or more physiological sensors comprises a sensor selected from the group consisting of a blood pressure monitor, a noninvasive blood pressure monitor, an electrocardiograph, a pulse oximeter, a transcranial Doppler sensor, an impedance cardiograph, a finometer, an infrared spectrometer, a capnography sensor, and a photoplethysmograph.

15. The method recited in claim 13 wherein the collected data values comprise data values selected from the group consisting of a perfusion index, a pleth variability index, cardiac output, heart stroke volume, arterial blood pressure, systolic blood pressure, diastolic blood pressure, mean arterial pressure, systolic pressure variability, pulse pressure, pulse pressure variability, stroke volume, cardiac index, and near-infrared spectroscopy data.

16. A system for predicting volume of acute blood loss from a patient that will cause CV collapse, the system comprising:

a physiological sensor interface configured to couple with one or more physiological sensors that collect physiological data values from the patient; and

a computational unit in communication communicatively coupled with the physiological sensor interface and having instructions to

received physiological data values from a patient through the physiological sensor interface; and

predict the volume of acute blood loss from the patient that will cause CV collapse by apply a hemodynamic compensation model to the physiological data values,

wherein the hemodynamic compensation model is generated from a plurality of data values previously collected from physiological sensors attached to a plurality of different subjects.

17. The system recited in claim 16 wherein the one or more physiological sensors comprises a sensor selected from the group consisting of an electrocardiograph, a pulse oximeter, transcranial Doppler sensor, capnography sensor, and a photoplethysmograph.

18. The system recited in claim 16 wherein the collected data values comprise data values selected from the group consisting of a perfusion index, a pleth variability index, cardiac output, heart stroke volume, arterial blood pressure, systolic blood pressure, diastolic blood pressure, mean arterial pressure, systolic pressure variability, pulse pressure, pulse pressure variability, stroke volume, cardiac index, and near-infrared spectroscopy data.

19. A method for determining a brain pressure within a subject, the method comprising:

measuring a plurality of parameters from the subject;

applying the parameters to a model that relates the parameters to the brain pressure, the model derived from application of a machine-learning algorithm; and

determining the brain pressure from the model.

20. The method recited in claim 19 wherein the brain pressure comprises an intracranial pressure.

21. The method recited in claim 19 wherein the brain pressure comprises a cerebral perfusion pressure.

22. The method recited in claim 19 wherein the plurality of parameters comprise heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, cardiac output, pulse oximetry data, or transcranial Doppler flow.

23. A method for predicting physiological phenomena, the method comprising:

receiving real-time physiological data from a physiological sensor that is measuring a physiological characteristic of a patient;

deriving physiological feature data from the physiological data;

determining a physiological threshold from the physiological feature data and from historical data, wherein the physiological threshold corresponds to a point such that when the physiological feature data reaches the physiological threshold abnormal physiology is deemed to be present; and

providing at a user interface the relationship between the physiological threshold and the physiological feature data as physiological feature data is derived from the physiological data.

24. The method according to claim 23 further comprising:

deriving second physiological feature data from the physiological data;

determining a second physiological threshold from the second physiological feature data and from historical data, wherein the second physiological threshold corresponds to a point such that when the second physiological feature data reaches the second physiological threshold a different physiological event occurs or is detected; and

providing at a user interface the relationship between the second physiological threshold and the physiological feature data as the second physiological feature data is derived.

25. The method according to claim 23, wherein the physiological threshold is determined using a predictive model.

26. The method according to claim 23, wherein the historical data is derived from a plurality of subjects.

27. The method according to claim 23, wherein the providing includes graphing the physiological threshold and the physiological feature data as a function of time. according to, wherein the physiological data comprises data selected from the list consisting of blood pressure data, EEG data, heart rate data, deoxygenated blood data, oxygenated blood data, muscular activity, and oxygen inhalation.

28. The method according to claim 23, wherein the physiological feature data comprises data selected from the list consisting of systolic blood pressure, arterial blood pressure, mean arterial blood pressure, pulse pressure variability, stroke volume, cardiac output, cardiac index, systolic pressure variability, and diastolic blood pressure.

29. The method according to claim 23, further comprising determining a physiological response to treatment by monitoring the convergence or divergence of the physiological threshold and the physiological feature data as a function of time.