  Objective: Medical relations are the core components of medical knowledge
graphs that are needed for healthcare artificial intelligence. However, the
requirement of expert annotation by conventional algorithm development
processes creates a major bottleneck for mining new relations. In this paper,
we present Hi-RES, a framework for high-throughput relation extraction
algorithm development. We also show that combining knowledge articles with
electronic health records (EHRs) significantly increases the classification
accuracy. Methods: We use relation triplets obtained from structured databases
and semistructured webpages to label sentences from target corpora as positive
training samples. Two methods are also provided for creating improved negative
samples by combining positive samples with na\"ive negative samples. We propose
a common model that summarizes sentence information using large-scale
pretrained language models and multi-instance attention, which then joins with
the concept embeddings trained from the EHRs for relation prediction. Results:
We apply the Hi-RES framework to develop classification algorithms for
disorder-disorder relations and disorder-location relations. Millions of
sentences are created as training data. Using pretrained language models and
EHR-based embeddings individually provides considerable accuracy increases over
those of previous models. Joining them together further tremendously increases
the accuracy to 0.947 and 0.998 for the two sets of relations, respectively,
which are 10-17 percentage points higher than those of previous models.
Conclusion: Hi-RES is an efficient framework for achieving high-throughput and
accurate relation extraction algorithm development.
