Ontology-driven and weakly supervised rare disease identification from clinical notes.

ABSTRACT:

Background

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.

Methods

We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven 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

SUBMITTER: Dong H 

PROVIDER: S-EPMC10162001 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature