Dong, Hang;
Suarez-Paniagua, Victor;
Zhang, Huayu;
Wang, Minhong;
Whitfield, Emma;
Wu, Honghan;
(2021)
Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision.
In:
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
(pp. pp. 2294-2298).
IEEE: Mexico.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision |
| Event: | 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) |
| Location: | ELECTR NETWORK |
| Dates: | 1 Nov 2021 - 5 Nov 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/EMBC46164.2021.9630043 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Engineering, Biomedical, Engineering, Electrical & Electronic, Engineering |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10147558 |
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