  Extracting phenotypes from clinical text has been shown to be useful for a
variety of clinical use cases such as identifying patients with rare diseases.
However, reasoning with numerical values remains challenging for phenotyping in
clinical text, for example, temperature 102F representing Fever. Current
state-of-the-art phenotyping models are able to detect general phenotypes, but
perform poorly when they detect phenotypes requiring numerical reasoning. We
present a novel unsupervised methodology leveraging external knowledge and
contextualized word embeddings from ClinicalBERT for numerical reasoning in a
variety of phenotypic contexts. Comparing against unsupervised benchmarks, it
shows a substantial performance improvement with absolute gains on generalized
Recall and F1 scores up to 79% and 71%, respectively. In the supervised
setting, it also surpasses the performance of alternative approaches with
absolute gains on generalized Recall and F1 scores up to 70% and 44%,
respectively.
