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

Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests