  The widespread digitization of patient data via electronic health records
(EHRs) has created an unprecedented opportunity to use machine learning
algorithms to better predict disease risk at the patient level. Although
predictive models have previously been constructed for a few important
diseases, such as breast cancer and myocardial infarction, we currently know
very little about how accurately the risk for most diseases or events can be
predicted, and how far in advance. Machine learning algorithms use training
data rather than preprogrammed rules to make predictions and are well suited
for the complex task of disease prediction. Although there are thousands of
conditions and illnesses patients can encounter, no prior research
simultaneously predicts risks for thousands of diagnosis codes and thereby
establishes a comprehensive patient risk profile. Here we show that such
pandiagnostic prediction is possible with a high level of performance across
diagnosis codes. For the tasks of predicting diagnosis risks both 1 and 6
months in advance, we achieve average areas under the receiver operating
characteristic curve (AUCs) of 0.803 and 0.758, respectively, across thousands
of prediction tasks. Finally, our research contributes a new clinical
prediction dataset in which researchers can explore how well a diagnosis can be
predicted and what health factors are most useful for prediction. For the first
time, we can get a much more complete picture of how well risks for thousands
of different diagnosis codes can be predicted.
