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Abstract / Description of output
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. Our best weakly supervised method achieved 81.4% precision and 91.4% recall on extracting rare disease UMLS phenotypes from the annotated MIMIC-III discharge summaries. 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.
Original language | English |
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Publisher | ArXiv |
DOIs | |
Publication status | Published - 1 May 2022 |
Keywords / Materials (for Non-textual outputs)
- clinical notes
- Natural Language Processing
- ontology matching
- phenotyping
- rare diseases
- weak supervision
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Dive into the research topics of 'Ontology-based and weakly supervised rare disease phenotyping from clinical notes'. Together they form a unique fingerprint.Projects
- 2 Active
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The Advanced Care Reseach Centre
UK industry, commerce and public corporations
1/04/20 → 31/03/27
Project: Research
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The Advanced Care Reseach Centre
UK industry, commerce and public corporations
1/04/20 → 31/03/27
Project: Research