  Large clinical datasets derived from insurance claims and electronic health
record (EHR) systems are valuable sources for precision medicine research.
These datasets can be used to develop models for personalized prediction of
risk or treatment response. Efficiently deriving prediction models using real
world data, however, faces practical and methodological challenges. Precise
information on important clinical outcomes such as time to cancer progression
are not readily available in these databases. The true clinical event times
typically cannot be approximated well based on simple extracts of billing or
procedure codes. Whereas, annotating event times manually is time and resource
prohibitive. In this paper, we propose a two-step semi-supervised multi-modal
automated time annotation (MATA) method leveraging multi-dimensional
longitudinal EHR encounter records. In step I, we employ a functional principal
component analysis approach to estimate the underlying intensity functions
based on observed point processes from the unlabeled patients. In step II, we
fit a penalized proportional odds model to the event time outcomes with
features derived in step I in the labeled data where the non-parametric
baseline function is approximated using B-splines. Under regularity conditions,
the resulting estimator of the feature effect vector is shown as root-$n$
consistent. We demonstrate the superiority of our approach relative to existing
approaches through simulations and a real data example on annotating lung
cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health
Administration.
