Methods and systems are disclosed for autonomously building a predictive model of outcomes. A most-predictive set of signals Sk is identified out of a set of signals s1, s2, . . . , sD for each of one or more outcomes ok. A set of probabilistic predictive models ôk=Mk(Sk) is autonomously learned, where ôk is a prediction of outcome ok derived from the model Mk that uses as inputs values obtained from the set of signals Sk. The step of autonomously learning is repeated incrementally from data that contains examples of values of signals s1, s2, . . . , sD and corresponding outcomes o1, o2, . . . , oK. Various embodiments are also disclosed that apply predictive models to various physiological events and to autonomous robotic navigation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicting cardiovascular collapse in a patient, the method comprising: receiving, at a computer, real-time, continuous pulsatile waveform data from one or more sensors that are measuring physiological characteristics of a patient; analyzing, with the computer, the real-time, continuous pulsatile waveform data with multiple linear probability density models generated by exposing a plurality of test subjects to simulated cardiovascular collapse, the models identifying one or more sensor signals as being most predictive of cardiovascular collapse, the one or more sensor signals representing continuous pulsatile waveform data; deriving, with the computer and from the linear probability density model, physiological feature data indicative of a probability that the patient will experience cardiovascular collapse; estimating, with the computer and using the multiple linear probability density model, a probability that the patient will experience cardiovascular collapse, based on the real-time, continuous pulsatile waveform data received from the one or more sensors; and displaying, with a display device, an estimate of the probability that the patient will experience cardiovascular collapse. 2. The method of claim 1, wherein the linear probability density model comprises a hemodynamic compensation model that is generated by: identifying a most-predictive set of signals Sk out of a set of signals s1, s2, . . . , sD for each of one or more outcomes Ok, each of the signals corresponding to data values collected from the plurality of test subjects; autonomously learning a set of probabilistic predictive models ôk=Mk(Sk), where ôk is a prediction of outcome Ok derived from the model Mk that uses as inputs values obtained from the set of signals Sk; and repeating the step of autonomously learning incrementally from data that contains examples of values of signals s1, S2, . . . , sD and corresponding outcomes o1, o2, . . . , oK. 3. The method of claim 2, wherein autonomously learning the set of probabilistic predictive models comprises using a linear model framework to identify predictive variables for each increment of data. 4. The method of claim 3, wherein the linear model framework is constructed with the form o ^ k = f k  ( a 0 + ∑ i = 1 d   a i  s i ) , where fk is a mapping function mapping one input to one output and a0, a1, . . . , ad are linear model coefficients. 5. The method of claim 1, wherein the physiological feature data reflects physiological information contained in the real-time, continuous pulsatile waveform data. 6. The method of claim 1, further comprising: determining a physiological response to treatment by monitoring the convergence or divergence to a physiological threshold of the real-time, continuous pulsatile waveform data and the physiological feature data as a function of time. 7. The method of claim 1, further comprising: displaying, with the display device and in real time, an indication of effectiveness of intravenous therapy. 8. The method of claim 1, wherein displaying, with the display device, an estimate of the probability that the patient will experience cardiovascular collapse comprises displaying a graph of a volume of acute blood loss of the patient and a volume of blood loss that will cause cardiovascular collapse as a function of time. 9. The method of claim 1, further comprising: deriving, with the computer and from the multiple linear probability density models, second physiological feature data; determining, with the computer and from the multiple linear probability density models, a physiological threshold from the second physiological feature data and from historical data, wherein the physiological threshold corresponds to a point such that when the second physiological feature data reaches the physiological threshold a different physiological event occurs or is detected; and displaying, with the display device, a relationship between the physiological threshold and the physiological feature data as the second physiological feature data is derived. 10. The method of claim 1, wherein the one or more sensors comprise one or more photoplethysmograph (“PPG”) sensors, one or more transcranial Doppler sensors, one or more noninvasive or invasive pulsatile sensors, one or more ECG sensors, and/or one or more blood flow sensors. 11. A system for predicting cardiovascular collapse in a patient, the system comprising: a physiological sensor interface configured to couple with one or more physiological sensors that collect physiological data values from a patient; and a processor having a non-transitory computer-readable storage medium, the processor in electrical communication with the sensor interface, the non-transitory computer-readable storage medium comprising instructions executable by the processor to: receive, via the physiological sensor interface, real-time, continuous pulsatile waveform data from one or more sensors that are measuring physiological characteristics of the patient; analyze the real-time, continuous pulsatile waveform data with multiple linear probability density models generated by exposing a plurality of test subjects to simulated cardiovascular collapse, the models identifying one or more sensor signals as being most predictive of cardiovascular collapse, the one or more sensor signals including continuous pulsatile waveform data; derive, from the linear probability density models, physiological feature data indicative of a probability that the patient will experience cardiovascular collapse; estimate, using the linear probability density models, a probability that the patient will experience cardiovascular collapse, based on the real-time, continuous pulsatile waveform data received from the one or more sensors; and display, with a display device in communication with the system, an estimate of the probability that the patient will experience cardiovascular collapse. 12. The system of claim 11, wherein the linear probability density model comprises a hemodynamic compensation model that is generated by: identifying a most-predictive set of signals Sk out of a set of signals s1, s2, . . . , sD for each of one or more outcomes ok, each of the signals corresponding to data values collected from the plurality of test subjects; autonomously learning a set of probabilistic predictive models ôk=Mk(Sk), where ôk is a prediction of outcome Ok derived from the model Mk that uses as inputs values obtained from the set of signals Sk; and repeating the step of autonomously learning incrementally from data that contains examples of values of signals s1, S2, . . . , sD and corresponding outcomes o1, o2, . . . , oK. 13. The system of claim 12, wherein autonomously learning the set of probabilistic predictive models comprises using a linear model framework to identify predictive variables for each increment of data. 14. The system of claim 13, wherein the linear model framework is constructed with the form o ^ k = f k  ( a 0 + ∑ i = 1 d   a i  s i ) , where fk is a mapping function mapping one input to one output and a0, a1, . . . , ad are linear model coefficients. 15. The system of claim 11, wherein the physiological feature data reflects physiological information contained in the real-time, continuous pulsatile waveform data. 16. The system of claim 11, wherein the instructions are further executable to: determine a physiological response to treatment by monitoring the convergence or divergence to a physiological threshold of the real-time, continuous pulsatile waveform data and the physiological feature data as a function of time. 17. The system of claim 11, wherein the instructions are further executable to: display, with the display device and in real time, an indication of effectiveness of intravenous therapy. 18. The system of claim 11, wherein the instructions executable to display, with the display device, an estimate of the probability that the patient will experience cardiovascular collapse comprises instructions executable to graph a volume of acute blood loss of the patient and a volume of blood loss that will cause cardiovascular collapse as a function of time. 19. The system of claim 11, wherein the instructions are further executable to: derive, from the multiple linear probability density model, second physiological feature data; determine, from the multiple linear probability density models, a physiological threshold from the second physiological feature data and from historical data, wherein the physiological threshold corresponds to a point such that when the second physiological feature data reaches the physiological threshold a different physiological event occurs or is detected; and display, with the display device, a relationship between the physiological threshold and the physiological feature data as the second physiological feature data is derived. 20. The system of claim 11, wherein the one or more sensors comprise one or more photoplethysmograph (“PPG”) sensors, one or more transcranial Doppler sensors, one or more noninvasive or invasive pulsatile sensors, one or more ECG sensors, and/or one or more blood flow sensors.