  Monitoring the health status of patients and predicting mortality in advance
is vital for providing patients with timely care and treatment. Massive medical
signs in electronic health records (EHR) are fitted into advanced machine
learning models to make predictions. However, the data-quality problem of
original clinical signs is less discussed in the literature. Based on an
in-depth measurement of the missing rate and correlation score across various
medical signs and a large amount of patient hospital admission records, we
discovered the comprehensive missing rate is extremely high, and a large number
of useless signs could hurt the performance of prediction models. Then we
concluded that only improving data-quality could improve the baseline accuracy
of different prediction algorithms. We designed MEDLENS, with an automatic
vital medical signs selection approach via statistics and a flexible
interpolation approach for high missing rate time series. After augmenting the
data-quality of original medical signs, MEDLENS applies ensemble classifiers to
boost the accuracy and reduce the computation overhead at the same time. It
achieves a very high accuracy performance of 0.96 AUC-ROC and 0.81 AUC-PR,
which exceeds the previous benchmark.
