Long Term Active Learning from Large Continually Changing Data Sets

摘要

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.

主权项

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.