Abstract
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 of discharge summaries and radiology reports from two institutions in the US and the UK. Our best weakly supervised method achieved 81.4% precision and 91.4% recall on extracting rare disease UMLS phenotypes from MIMIC-III discharge summaries. The overall pipeline processing clinical notes can surface rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. We discuss the usefulness of the weak supervision approach and propose directions for future studies.
Abstract (translated)
URL
https://arxiv.org/abs/2205.05656
