  The identification of rare diseases from clinical notes with Natural Language
Processing (NLP) is challenging due to the few cases available for machine
learning and the need of data annotation from clinical experts. We propose a
method using ontologies and weak supervision. The approach includes two steps:
(i) Text-to-UMLS, linking text mentions to concepts in Unified Medical Language
System (UMLS), with a named entity linking tool (e.g. SemEHR) and weak
supervision based on customised rules and Bidirectional Encoder Representations
from Transformers (BERT) based contextual representations, and (ii)
UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease
Ontology (ORDO). Using MIMIC-III US intensive care discharge summaries as a
case study, we show that the Text-to-UMLS process can be greatly improved with
weak supervision, without any annotated data from domain experts. Our analysis
shows that the overall pipeline processing discharge summaries can surface rare
disease cases, which are mostly uncaptured in manual ICD codes of the hospital
admissions.
