  Electronic health record (EHR) data are increasingly used to support
real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is
limited by the lack of readily available precise information on the timing of
clinical events such as the onset time of heart failure. We propose a
LAbel-efficienT incidenT phEnotyping (LATTE) algorithm to accurately annotate
the timing of clinical events from longitudinal EHR data. By leveraging the
pre-trained semantic embedding vectors from large-scale EHR data as prior
knowledge, LATTE selects predictive EHR features in a concept re-weighting
module by mining their relationship to the target event and compresses their
information into longitudinal visit embeddings through a visit attention
learning network. LATTE employs a recurrent neural network to capture the
sequential dependency between the target event and visit embeddings
before/after it. To improve label efficiency, LATTE constructs highly
informative longitudinal silver-standard labels from large-scale unlabeled
patients to perform unsupervised pre-training and semi-supervised joint
training. Finally, LATTE enhances cross-site portability via contrastive
representation learning. LATTE is evaluated on three analyses: the onset of
type-2 diabetes, heart failure, and the onset and relapses of multiple
sclerosis. We use various evaluation metrics present in the literature
including the $ABC_{gain}$, the proportion of reduction in the area between the
observed event indicator and the predicted cumulative incidences in reference
to the prediction per incident prevalence. LATTE consistently achieves
substantial improvement over benchmark methods such as SAMGEP and RETAIN in all
settings.
