1. A computer-implemented method for supporting extubation management, the method comprising: accessing a waveform dataset database to obtain waveform data previously collected from a plurality of patients, the waveform data comprising waveforms associated with patients that experienced one of a plurality of extubation outcomes; obtaining from one or more variability analyzers, waveform data and a plurality of variability metrics in a variability data set indicative of a degree and character to which at least one patient parameter changes over intervals of time for the plurality of patients; computing a plurality of different predictive models using the variability data and the waveform data obtained from the database; applying the waveform data and the variability data set for a corresponding patient to each of the plurality of predictive models to obtain a plurality of logistic regression predictive models indicative of an extubation outcome by selecting measures of variability from the variability data set and determining at least one threshold for determining the extubation outcome, each logistic regression providing a probability of the extubation outcome; averaging the plurality of logistic regression predictive models to generate an ensemble average of logistic regression predictive models indicating a final probability of the extubation outcome; and displaying via a user interface and/or outputting via a report, at least one decision support index determined from the ensemble of logistic regression predictive models that is indicative of the final probability of the extubation outcome, along with standard clinical factors for the corresponding patient, at least one of the standard clinical factors normally used to evaluate readiness for extubation, wherein the displaying is performed in a clinical location where the extubation is performed to assist in a clinical decision related to the extubation.