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Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
[article]
2023
arXiv
pre-print
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,
arXiv:2205.05656v4
fatcat:3cc3b6gn7zdhzalag77aryzpt4