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Anesthesiologists are taught to carefully manage patient vital signs during surgery. Unfortunately, there is little empirical evidence that vital sign management, as currently practiced, is correlated with patient outcomes. We seek to validate or repudiate current clinical practice. Using a database of over 90,000 cases, we attempt to determine whether those cases that an anesthesiologist would subjectively decide are “low quality” are more likely to result in negative outcomes. The problem reduces to one of multidimensional time series classification. Our approach is to have an expert anesthesiologist label a small number of training cases, from which we can train a classifier to use to label all 90,000 cases. We then use the labeling to search for correlation with outcomes. We consider several standard classification methods, such as dynamic time warping in conjunction with a kNN classifier, as well as the recently proposed complexity invariant distance, and a regression based upon the feature extraction methods outlined by Mao et al. (using features such as time series mean, standard deviation, skew, approximate entropy, etc.). We also propose a feature selection mechanism that learns a hidden Markov model to segment the time series; the fraction of time that each series spends in each state is used to label the series using a regression based classifier. In the end, we are able to obtain strong, empirical evidence that current best practice is correlated with reduced negative patient outcomes.

Correlating Surgical Vital Sign Quality with 30-Day Outcomes using Regression on Time Series Segment Features