UNASSIGNED: Influenza is a yearly recurrent disease that has the potential to become a pandemic. An effective biosurveillance system is required for early detection of the disease. In our previous studies, we have shown that electronic @entity341 ( @entity341 ) free-text reports can be of value to improve @entity683 detection in real time. This paper studies seven machine learning (ML) classifiers for @entity683 detection, compares their diagnostic capabilities against an expert-built @entity683 Bayesian classifier, and evaluates different ways of handling missing clinical information from the free-text reports. We randomly identified 31,268 @entity341 reports from 4 hospitals between 2008 and 2011 to form two different datasets: training (468 cases, 29,004 controls), and test (176 cases and 1620 controls). We employed Topaz, a natural language processing (NLP) tool, to extract @entity683 -related findings and to encode them into one of three values: Acute, non-acute, and missing. Results show that all ML classifiers had areas under ROCs (AUC) ranging from 0.88 to 0.93, and performed significantly better than the expert-built Bayesian model. Missing clinical information marked as a value of missing (not missing at random) had a consistently improved performance among 3 (out of 4) ML classifiers when it was compared with the configuration of not assigning a value of missing (missing completely at random). The case/control ratios did not affect the classification performance given the large number of training cases. Our study demonstrates @entity341 reports in conjunction with the use of ML and NLP with the handling of missing value information have a great potential for the detection of @entity166 .

Comparison of machine learning classifiers for XXXX detection from emergency department free-text reports.

@entity683 :: ('MESH:D007251', 'Disease') :: ['influenza'] @entity341 :: ('MESH:D004630', 'Disease') :: ['Emergency Department', 'ED'] @entity166 :: ('MESH:D003141', 'Disease') :: ['infectious diseases']
@entity683 :: (MESH:D007251,Disease) :: ['influenza']
