  Disparate areas of machine learning have benefited from models that can take
raw data with little preprocessing as input and learn rich representations of
that raw data in order to perform well on a given prediction task. We evaluate
this approach in healthcare by using longitudinal measurements of lab tests,
one of the more raw signals of a patient's health state widely available in
clinical data, to predict disease onsets. In particular, we train a Long
Short-Term Memory (LSTM) recurrent neural network and two novel convolutional
neural networks for multi-task prediction of disease onset for 133 conditions
based on 18 common lab tests measured over time in a cohort of 298K patients
derived from 8 years of administrative claims data. We compare the neural
networks to a logistic regression with several hand-engineered, clinically
relevant features. We find that the representation-based learning approaches
significantly outperform this baseline. We believe that our work suggests a new
avenue for patient risk stratification based solely on lab results.
