  In healthcare, the highest risk individuals for morbidity and mortality are
rarely those with the greatest modifiable risk. By contrast, many machine
learning formulations implicitly attend to the highest risk individuals. We
focus on this problem in point processes, a popular modeling technique for the
analysis of the temporal event sequences in electronic health records (EHR)
data with applications in risk stratification and risk score systems. We show
that optimization of the log-likelihood function also gives disproportionate
attention to high risk individuals and leads to poor prediction results for low
risk individuals compared to ones at high risk. We characterize the problem and
propose an adjusted log-likelihood formulation as a new objective for point
processes. We demonstrate the benefits of our method in simulations and in EHR
data of patients admitted to the critical care unit for intracerebral
hemorrhage.
