  Computational text phenotyping is the practice of identifying patients with
certain disorders and traits from clinical notes. Rare diseases are challenging
to be identified due to few cases available for machine learning and the need
for data annotation from domain experts. We propose a method using ontologies
and weak supervision, with recent pre-trained contextual representations from
Bi-directional Transformers (e.g. BERT). The ontology-based framework includes
two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking
mentions to concepts in Unified Medical Language System (UMLS), with a Named
Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with
customised rules and contextual mention representation; (ii) UMLS-to-ORDO,
matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology
(ORDO). The weakly supervised approach is proposed to learn a phenotype
confirmation model to improve Text-to-UMLS linking, without annotated data from
domain experts. We evaluated the approach on three clinical datasets, MIMIC-III
discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging
reports from two institutions in the US and the UK, with annotations. The
improvements in the precision were pronounced (by over 30% to 50% absolute
score for Text-to-UMLS linking), with almost no loss of recall compared to the
existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and
NHS Tayside were consistent with the discharge summaries. The overall pipeline
processing clinical notes can extract rare disease cases, mostly uncaptured in
structured data (manually assigned ICD codes). We discuss the usefulness of the
weak supervision approach and propose directions for future studies.
