  Early diagnosis of treatable diseases is essential for improving healthcare,
and many diseases' onsets are predictable from annual lab tests and their
temporal trends. We introduce a multi-resolution convolutional neural network
for early detection of multiple diseases from irregularly measured sparse lab
values. Our novel architecture takes as input both an imputed version of the
data and a binary observation matrix. For imputing the temporal sparse
observations, we develop a flexible, fast to train method for differentiable
multivariate kernel regression. Our experiments on data from 298K individuals
over 8 years, 18 common lab measurements, and 171 diseases show that the
temporal signatures learned via convolution are significantly more predictive
than baselines commonly used for early disease diagnosis.
