A machine learning approach to triaging patients with chronic obstructive pulmonary disease.

ABSTRACT: COPD patients are burdened with a daily risk of acute exacerbation and loss of control, which could be mitigated by effective, on-demand decision support tools. In this study, we present a machine learning-based strategy for early detection of exacerbations and subsequent triage. Our application uses physician opinion in a statistically and clinically comprehensive set of patient cases to train a supervised prediction algorithm. The accuracy of the model is assessed against a panel of physicians each triaging identical cases in a representative patient validation set. Our results show that algorithm accuracy and safety indicators surpass all individual pulmonologists in both identifying exacerbations and predicting the consensus triage in a 101 case validation set. The algorithm is also th

SUBMITTER: Swaminathan S 

PROVIDER: S-EPMC5699810 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature